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Mei Y, Li A, Zhao J, Li Y, Zhou Q, Yang M, Zhao M, Xu J, Li K, Yin G, Wu J, Xu Q. Disturbed glucose homeostasis and its increased allostatic load in response to individual, joint and fluctuating air pollutants exposure: Evidence from a longitudinal study in prediabetes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175498. [PMID: 39151627 DOI: 10.1016/j.scitotenv.2024.175498] [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: 06/04/2024] [Revised: 07/30/2024] [Accepted: 08/12/2024] [Indexed: 08/19/2024]
Abstract
We investigated the effect of individual, joint and fluctuating exposure to air pollution (PM2.5, BC, NO3-, NH4+, OM, SO42-, PM10, NO2, SO2, O3) on glucose metabolisms among prediabetes, and simultaneously explored the modifying effect of lifestyle. We conducted a longitudinal study among prediabetes during 2018-2022. Exposure windows within 60-days moving averages and their variabilities were calculated. FBG, insulin, HOMA-IR, HOMA-B, triglyceride glucose index (TyG), glucose insulin ratio (GI) and allostatic load of glucose homeostasis system (AL-GHS) was included. Linear mixed-effects model and BKMR were adopted to investigate the individual and overall effects, respectively. We also explored the preventive role of lifestyle. Individual air pollutant was associated with increased FBG, insulin, HOMA-IR, HOMA-B, TyG, and decreased GI. People with FBG ≥6.1 mmol/L were more susceptible. Air pollutants mixture were only associated with increased HOMA-B, and constituents have the highest group-PIP. Air pollutants variation also exert harmful effect. We observed similar diabetic effect on AL-GHS. Finally, the diabetic effect of air pollutants disappeared if participants adopt a favorable lifestyle. Our findings highlighted the importance of comprehensively assessing multiple air pollutants and their variations, focusing on metabolic health status in the early prevention of T2D, and adopting healthy lifestyle to mitigate such harmful effect.
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Affiliation(s)
- Yayuan Mei
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Big Data Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100046, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Ang Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Jiaxin Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Yanbing Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Quan Zhou
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Ming Yang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Meiduo Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Jing Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Kai Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Guohuan Yin
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Jingtao Wu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Qun Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
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Ma Y, Sun X, Yao X. The role and mechanism of VDAC1 in type 2 diabetes: An underestimated target of environmental pollutants. Mitochondrion 2024; 78:101929. [PMID: 38986923 DOI: 10.1016/j.mito.2024.101929] [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: 02/26/2024] [Revised: 06/08/2024] [Accepted: 07/07/2024] [Indexed: 07/12/2024]
Abstract
Type 2 diabetes (T2D) is a chronic metabolic disease that accounts for more than 90% of diabetic patients. Its main feature is hyperglycemia due to insulin resistance or insulin deficiency. With changes in diet and lifestyle habits, the incidence of T2D in adolescents has burst in recent decades. The deterioration in the exposure to the environmental pollutants further aggravates the prevalence of T2D, and consequently, it imposes a significant economic burden. Therefore, early prevention and symptomatic treatment are essential to prevent diabetic complications. Mitochondrial number and electron transport chain activity are decreased in the patients with T2D. Voltage-Dependent Anion Channel 1 (VDAC1), as a crucial channel protein on the outer membrane of mitochondria, regulates signal transduction between mitochondria and other cellular components, participating in various biological processes. When VDAC1 exists in oligomeric form, it additionally facilitates the entry and exit of macromolecules into and from mitochondria, modulating insulin secretion. We summarize and highlight the interplay between VDAC1 and T2D, especially in the environmental pollutants-related T2D, shed light on the potential therapeutic implications of targeting VDAC1 monomers and oligomers, providing a new possible target for the treatment of T2D.
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Affiliation(s)
- Yu Ma
- Environmental and Occupational Health Department, Dalian Medical University, 9 West Lushun South Road, Dalian, China
| | - Xiance Sun
- Environmental and Occupational Health Department, Dalian Medical University, 9 West Lushun South Road, Dalian, China
| | - Xiaofeng Yao
- Environmental and Occupational Health Department, Dalian Medical University, 9 West Lushun South Road, Dalian, China.
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Liu X, Liu X, Jin M, Huang N, Song Z, Li N, Huang T. Association between birth weight/joint exposure to ambient air pollutants and type 2 diabetes: a cohort study in the UK Biobank. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2024; 34:2888-2898. [PMID: 37936397 DOI: 10.1080/09603123.2023.2278634] [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: 05/22/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023]
Abstract
Early life events and environmental factors are associated with type 2 diabetes (T2D) development. We assessed the combined effect of birth weight andambient air pollutants, and effect of their interaction on T2D risk. Totally, 6,474 T2D incidents were recorded over an 8.7-year follow-up period. The adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were 1.31 (1.26, 1.36) for each kilogram decrease in birth weight, and 1.08 (1.05, 1.11) for each standard deviation increase in air pollution score (APS). Birth weight<3000 g amplified the T2D risk associated with high APS. A combination of the lowest birth weight (<2500 g) and the highest quintile of APS led to over two-fold increase in T2D risk (aHR: 2.17; 95% CI: 1.79-2.64). There was a significant additive interaction between them. In conclusion, ambient air pollutants increase the risk for T2D, particularly in populations with low birth weight.
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Affiliation(s)
- Xiaojing Liu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Institute of Reproductive and Child Health, Peking University/Key Laboratory of Reproductive Health, Beijing, China
| | - Xiaowen Liu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Institute of Reproductive and Child Health, Peking University/Key Laboratory of Reproductive Health, Beijing, China
| | - Ming Jin
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Institute of Reproductive and Child Health, Peking University/Key Laboratory of Reproductive Health, Beijing, China
| | - Ninghao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zimin Song
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Nan Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Institute of Reproductive and Child Health, Peking University/Key Laboratory of Reproductive Health, Beijing, China
| | - Tao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Ministry of Education, Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Beijing, China
- Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing, China
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Zhu Y, Wu Y, Cheng J, Liang H, Chang Q, Lin F, Li D, Zhou X, Chen X, Pan P, Liu H, Guo Y, Zhang Y. Ambient air pollution, lifestyle, and genetic predisposition on all-cause and cause-specific mortality: A prospective cohort study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:173120. [PMID: 38750765 DOI: 10.1016/j.scitotenv.2024.173120] [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: 01/20/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Although it is widely acknowledged that long-term exposure to ambient air pollution is closely related to the risk of mortality, there were inconsistencies in terms of cause-specific mortality and it is still unknown whether lifestyle and genetic susceptibility could modify the association. METHODS This population-based prospective cohort study involved 461,112 participants from the UK Biobank. The land-use regression model was used to estimate the concentrations of particulate matter (PM2.5, PMcoarse, PM10), and nitrogen oxides (NO2 and NOx). The association between air pollution and mortality was evaluated using Cox proportional hazard models. Furthermore, a lifestyle score incorporated with smoking status, physical activity, alcohol consumption, and diet behaviors, and polygenic risk score using 12 genetic variants, were developed to assess the modifying effect of air pollution on mortality outcomes. RESULTS During a median follow-up of 14.0 years, 33,903 deaths were recorded, including 17,083 (2835; 14,248), 6970, 2429, and 1287 deaths due to cancer (lung cancer, non-lung cancer), cardiovascular disease (CVD), respiratory and digestive disease, respectively. Each interquartile range (IQR) increase in PM2.5, NO2 and NOx was associated with 7 %, 6 % and 5 % higher risk of all-cause mortality, respectively. Specifically, for cause-specific mortality, each IQR increase in PM2.5, NO2 and NOx was also linked to mortality due to cancer (lung cancer and non-lung cancer), CVD, respiratory and digestive disease. Furthermore, additive and multiplicative interactions were identified between high ambient air pollution and unhealthy lifestyle on mortality. In addition, associations between air pollution and mortality were modified by lifestyle behaviors. CONCLUSION Long-term exposure to air pollutants increased the risk of all-cause and cause-specific mortality, which was modified by lifestyle behaviors. In addition, we also revealed a synergistically detrimental effect between air pollution and an unhealthy lifestyle, suggesting the significance of joint air pollution management and adherence to a healthy lifestyle on public health.
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Affiliation(s)
- Yiqun Zhu
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Yao Wu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jun Cheng
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Huaying Liang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Qinyu Chang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Fengyu Lin
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Dianwu Li
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China
| | - Xin Zhou
- Department of Dermatology, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China
| | - Xiang Chen
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia; Department of Dermatology, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, Hunan, China
| | - Pinhua Pan
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, Hunan, China
| | - Hong Liu
- Department of Dermatology, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, Hunan, China
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yan Zhang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China; Center of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha 410008, Hunan, China.
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5
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Zheng J, Yang Q, Huang J, Chen H, Shen J, Tang S. Hospital-treated infectious diseases, genetic susceptibility and risk of type 2 diabetes: A population-based longitudinal study. Diabetes Metab Syndr 2024; 18:103063. [PMID: 38917709 DOI: 10.1016/j.dsx.2024.103063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND The longitudinal association between infectious diseases and the risk of type 2 diabetes (T2D) remains unclear. METHODS Based on the UK Biobank, the prospective cohort study included a total of 396,080 participants without diabetes at baseline. We determined the types and sites of infectious diseases and incident T2D using the International Classification of Diseases 10th Revision codes (ICD-10). Time-varying Cox proportional hazard model was used to assess the association. Infection burden was defined as the number of infection episodes over time and the number of co-occurring infections. Genetic risk score (GRS) for T2D consisted of 424 single nucleotide polymorphisms. RESULTS During a median of 9.04 [IQR, 8.3-9.7] years of follow-up, hospital-treated infectious diseases were associated with a greater risk of T2D (adjusted HR [aHR] 1.54 [95 % CI 1.46-1.61]), with risk difference per 10,000 individuals equal to 154.1 [95 % CI 140.7-168.2]. The heightened risk persisted after 5 years following the index infection. Bacterial infection with sepsis had the strongest risk of T2D (aHR 2.95 [95 % CI 2.53-3.44]) among different infection types. For site-specific analysis, bloodstream infections posed the greatest risk (3.01 [95 % CI 2.60-3.48]). A dose-response association was observed between infection burden and T2D risk within each GRS tertile (p-trend <0.001). High genetic risk and infection synergistically increased the T2D risk. CONCLUSION Infectious diseases were associated with an increased risk of subsequent T2D. The risk showed specificity according to types, sites, severity of infection and the period since infection occurred. A potential accumulative effect of infection was revealed.
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Affiliation(s)
- Jiazhen Zheng
- Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
| | - Quan Yang
- Cardiac and Vascular Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Jinghan Huang
- Biomedical Genetics Section, School of Medicine, Boston University, China; Department of Chemical Pathology, Faculty of Medicine, The Chinese University of Hong Kong, China
| | - Hengying Chen
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junchun Shen
- Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
| | - Shaojun Tang
- Bioscience and Biomedical Engineering Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China; Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
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Jiang F, Zhao J, Sun J, Chen W, Zhao Y, Zhou S, Yuan S, Timofeeva M, Law PJ, Larsson SC, Chen D, Houlston RS, Dunlop MG, Theodoratou E, Li X. Impact of ambient air pollution on colorectal cancer risk and survival: insights from a prospective cohort and epigenetic Mendelian randomization study. EBioMedicine 2024; 103:105126. [PMID: 38631091 PMCID: PMC11035091 DOI: 10.1016/j.ebiom.2024.105126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 03/20/2024] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND This study investigates the associations between air pollution and colorectal cancer (CRC) risk and survival from an epigenomic perspective. METHODS Using a newly developed Air Pollutants Exposure Score (APES), we utilized a prospective cohort study (UK Biobank) to investigate the associations of individual and combined air pollution exposures with CRC incidence and survival, followed by an up-to-date systematic review with meta-analysis to verify the associations. In epigenetic two-sample Mendelian randomization analyses, we examine the associations between genetically predicted DNA methylation related to air pollution and CRC risk. Further genetic colocalization and gene-environment interaction analyses provided different insights to disentangle pathogenic effects of air pollution via epigenetic modification. FINDINGS During a median 12.97-year follow-up, 5767 incident CRC cases among 428,632 participants free of baseline CRC and 533 deaths in 2401 patients with CRC were documented in the UK Biobank. A higher APES score was associated with an increased CRC risk (HR, 1.03, 95% CI = 1.01-1.06; P = 0.016) and poorer survival (HR, 1.13, 95% CI = 1.03-1.23; P = 0.010), particularly among participants with insufficient physical activity and ever smokers (Pinteraction > 0.05). A subsequent meta-analysis of seven observational studies, including UK Biobank data, corroborated the association between PM2.5 exposure (per 10 μg/m3 increment) and elevated CRC risk (RR,1.42, 95% CI = 1.12-1.79; P = 0.004; I2 = 90.8%). Genetically predicted methylation at PM2.5-related CpG site cg13835894 near TMBIM1/PNKD and cg16235962 near CXCR5, and NO2-related cg16947394 near TMEM110 were associated with an increased CRC risk. Gene-environment interaction analysis confirmed the epigenetic modification of aforementioned CpG sites with CRC risk and survival. INTERPRETATION Our study suggests the association between air pollution and CRC incidence and survival, underscoring the possible modifying roles of epigenomic factors. Methylation may partly mediate pathogenic effects of air pollution on CRC, with annotation to epigenetic alterations in protein-coding genes TMBIM1/PNKD, CXCR5 and TMEM110. FUNDING Xue Li is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001), the National Nature Science Foundation of China (No. 82204019) and Healthy Zhejiang One Million People Cohort (K-20230085). ET is supported by a Cancer Research UK Career Development Fellowship (C31250/A22804). MGD is supported by the MRC Human Genetics Unit Centre Grant (U127527198).
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Affiliation(s)
- Fangyuan Jiang
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianhui Zhao
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Sun
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenxi Chen
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuyuan Zhao
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Siyun Zhou
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Maria Timofeeva
- Danish Institute for Advanced Study (DIAS), Epidemiology, Biostatistics and Biodemography Research Unit, Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Philip J Law
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden; Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala, Sweden
| | - Dong Chen
- Department of Colorectal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China
| | - Richard S Houlston
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Malcolm G Dunlop
- Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK; Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Xue Li
- Department of Big Data in Health Science, School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Lu X, Xie T, van Faassen M, Kema IP, van Beek AP, Xu X, Huo X, Wolffenbuttel BHR, van Vliet-Ostaptchouk JV, Nolte IM, Snieder H. Effects of endocrine disrupting chemicals and their interactions with genetic risk scores on cardiometabolic traits. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169972. [PMID: 38211872 DOI: 10.1016/j.scitotenv.2024.169972] [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: 11/10/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
Ubiquitous non-persistent endocrine disrupting chemicals (EDCs) have inconsistent associations with cardiometabolic traits. Additionally, large-scale genome-wide association studies (GWASs) have yielded many genetic risk variants for cardiometabolic traits and diseases. This study aimed to investigate the associations between a wide range of EDC exposures (parabens, bisphenols, and phthalates) and 14 cardiometabolic traits and whether these are moderated by their respective genetic risk scores (GRSs). Data were from 1074 participants aged 18 years or older of the Lifelines Cohort Study, a large population-based biobank. GRSs for 14 cardiometabolic traits were calculated based on genome-wide significant common variants from recent GWASs. The concentrations of 15 EDCs in 24-hour urine were measured by isotope dilution liquid chromatography tandem mass spectrometry technology. The main effects of trait-specific GRSs and each of the EDC exposures and their interaction effects on the 14 cardiometabolic traits were examined in multiple linear regression. The present study confirmed significant main effects for all GRSs on their corresponding cardiometabolic trait. Regarding the main effects of EDC exposures, 26 out of 280 EDC-trait tests were significant with explained variances ranging from 0.43 % (MMP- estimated glomerular filtration rate (eGFR)) to 2.37 % (PrP-waist-hip ratio adjusted body mass index (WHRadjBMI)). We confirmed the association of MiBP and MBzP with WHRadjBMI and body mass index (BMI), and showed that parabens, bisphenol F, and many other phthalate metabolites significantly contributed to the variance of WHRadjBMI, BMI, high-density lipoprotein (HDL), eGFR, fasting glucose (FG), and diastolic blood pressure (DBP). Only one association between BMI and bisphenol F was nominally significantly moderated by the GRS explaining 0.36 % of the variance. However, it did not survive multiple testing correction. We showed that non-persistent EDC exposures exerted effects on BMI, WHRadjBMI, HDL, eGFR, FG, and DBP. However no evidence for a modulating role of GRSs was found.
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Affiliation(s)
- Xueling Lu
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands; Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Martijn van Faassen
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Ido P Kema
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - André P van Beek
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Xijin Xu
- Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Xia Huo
- Laboratory of Environmental Medicine and Developmental Toxicology, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 510632, Guangdong, China
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Jana V van Vliet-Ostaptchouk
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands.
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Bi J, Liu Q, Fan G, Fang Q, Zhang X, Qin X, Wu M, Wan Z, Lv Y, Wang Y, Song L. Exposure to organochlorine pesticides and polychlorinated biphenyls, adherence to an ideal cardiovascular health, and arterial stiffness among Chinese adults. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2023; 46:10. [PMID: 38142250 DOI: 10.1007/s10653-023-01791-6] [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: 07/18/2023] [Accepted: 11/17/2023] [Indexed: 12/25/2023]
Abstract
This study aimed to assess the relationships between exposure to individual organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and their mixture and arterial stiffness and explore whether adherence to an ideal cardiovascular health (CVH) could mitigate these associations. The cross-sectional study enrolled 1437 Chinese adults between March and May 2019 in Wuhan, China. OCPs and PCBs concentrations were measured using solid phase extraction coupled with gas chromatography-tandem mass spectrometry. Arterial stiffness was evaluated by brachial-ankle pulse wave velocity (baPWV). CVH was determined by three behavioral and four biological metrics and categorized as ideal, intermediate, and poor CVH. We applied generalized linear model and weighted quantile sum (WQS) regression to evaluate the associations of exposure to individual OCPs or PCBs and their mixture with baPWV, respectively. We found that participants with detectable levels of heptachlor epoxide, PCB-153, and PCB-180 had higher baPWV (β: 34.25, 95% CI 14.28-54.22; β: 27.64, 95% CI 7.90-47.38; and β: 30.51, 95% CI 10.68-50.35) than those with undetectable levels. In WQS regression, the mixture of OCPs and PCBs was related to a higher baPWV (β: 24.93, 95% CI 2.70-47.15). Compared with participants with ideal CVH and undetectable OCPs or PCBs levels, those with poor CVH and detectable OCPs or PCBs levels had the highest increase in baPWV (heptachlor epoxide: β: 147.94, 95% CI 112.52-183.55; PCB-153: β: 150.22, 95% CI 115.40-185.04; PCB-180: β: 147.02, 95% CI 111.66-182.38). Our findings suggested that individual OCPs, PCBs, and their mixture exposure were positively associated with arterial stiffness, and adherence to an ideal CVH may mitigate the adverse effect.
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Affiliation(s)
- Jianing Bi
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qing Liu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Gaojie Fan
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qing Fang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xukuan Zhang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiya Qin
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mingyang Wu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhengce Wan
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yongman Lv
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Youjie Wang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China.
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Lulu Song
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China.
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Jiang Z, Zhang S, Chen K, Wu Y, Zeng P, Wang T. Long-term influence of air pollutants on morbidity and all-cause mortality of cardiometabolic multi-morbidity: A cohort analysis of the UK Biobank participants. ENVIRONMENTAL RESEARCH 2023; 237:116873. [PMID: 37573024 DOI: 10.1016/j.envres.2023.116873] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 07/19/2023] [Accepted: 08/10/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND The effects of air pollutants on cardiometabolic diseases (CMDs) have been widely explored, whereas their influences on cardiometabolic multi-morbidity (CMM) were not clear. METHODS We employed the UK Biobank cohort (N = 317,160) to study the association between six air pollutants (PM2.5, PM10, PM2.5-10, PM2.5abs, NO2, and NOx) and four CMDs including type II diabetes (T2D), coronary artery disease (CAD), stroke and hypertension. CMM was defined as occurrence of two or more of the four diseases. Multi-state Cox models were performed to estimate hazard ratio (HR) and its 95% confidence interval (95%CI). RESULTS During a median follow-up of 12.8 years, 52,211 participants developed only one CMD, 15,446 further developed CMM, and 16,861 ultimately died. It was demonstrated that per interquartile range increase (IQR) increases in PM2.5, PM10, PM2.5-10, PM2.5abs, NO2, and NOx would increase 12% (9%-15%), 4% (1%-7%), 3% (1%-6%), 7% (4%-10%), 11% (8%-15%) and 10% (7%-13%) higher risk of developing one CMD from health baseline; 7% (2%-12%), 8% (3%-13%), 6% (2%-11%), 10% (5%-15%), 13% (7%-18%) and 10% (5%-15%) greater risk of occurring CMM from one CMD baseline; and 11% (-2%∼26%), 22% (7%-38%), 17% (3%-32%), 31% (16%-49%), 33% (17%-51%) and 32% (17%-50%) larger risk of causing death from CMM baseline, respectively. CONCLUSIONS We revealed that people living in areas with high air pollution suffered from higher hazard of CMD, CMM and all-cause mortality; our findings implied keeping clean air was an effective approach to prevent or mitigate initiation, progression, and death from healthy to CMDs and from CMDs to CMM.
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Affiliation(s)
- Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Yuxuan Wu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China; Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
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Tsalenchuk M, Gentleman SM, Marzi SJ. Linking environmental risk factors with epigenetic mechanisms in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:123. [PMID: 37626097 PMCID: PMC10457362 DOI: 10.1038/s41531-023-00568-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Sporadic Parkinson's disease (PD) is a progressive neurodegenerative disease, with a complex risk structure thought to be influenced by interactions between genetic variants and environmental exposures, although the full aetiology is unknown. Environmental factors, including pesticides, have been reported to increase the risk of developing the disease. Growing evidence suggests epigenetic changes are key mechanisms by which these environmental factors act upon gene regulation, in disease-relevant cell types. We present a systematic review critically appraising and summarising the current body of evidence of the relationship between epigenetic mechanisms and environmental risk factors in PD to inform future research in this area. Epigenetic studies of relevant environmental risk factors in animal and cell models have yielded promising results, however, research in humans is just emerging. While published studies in humans are currently relatively limited, the importance of the field for the elucidation of molecular mechanisms of pathogenesis opens clear and promising avenues for the future of PD research. Carefully designed epidemiological studies carried out in PD patients hold great potential to uncover disease-relevant gene regulatory mechanisms. Therefore, to advance this burgeoning field, we recommend broadening the scope of investigations to include more environmental exposures, increasing sample sizes, focusing on disease-relevant cell types, and recruiting more diverse cohorts.
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Affiliation(s)
- Maria Tsalenchuk
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK.
- Department of Brain Sciences, Imperial College London, London, UK.
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Zou H, Zhang S, Cai M, Qian ZM, Zhang Z, Chen L, Wang X, Arnold LD, Howard SW, Li H, Lin H. Ambient air pollution associated with incidence and progression trajectory of cardiometabolic diseases: A multi-state analysis of a prospective cohort. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 862:160803. [PMID: 36493826 DOI: 10.1016/j.scitotenv.2022.160803] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Previous studies on the association between ambient air pollution and cardiometabolic diseases (CMDs) focused on a single disease, without considering cardiometabolic multimorbidity (CMM) and the progression trajectory of CMDs. METHODS Based on the UK Biobank cohort, we included 372,530 participants aged 37-73 years at baseline (2006-2010) with follow-up until September 2021. Incident CMDs cases were identified based on self-reported information and multiple health-related records in the UK Biobank. CMM was defined as the occurrence of at least two CMDs, including ischemic heart disease (IHD), stroke and type 2 diabetes (T2D). Exposure to ambient air pollutants, including particulate matter (PM) with aerodynamic diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were estimated at participants' geocoded residential addresses based on the high-resolution (1 × 1 km) pollution data from 2001 to 2021 provided by UK Department for Environment, Food and Rural Affairs. Multi-state models with adjustment for potential confounders were used to examine the impact of long-term exposure to ambient air pollution on transitions from healthy to first CMD (FCMD), subsequently to CMM, and further to death. RESULTS During a median follow-up of 12.6 years, 40,112 participants developed at least one CMD, 3896 developed CMM, and 21,739 died. Among the four pollutants, PM2.5 showed the strongest associations with all transitions from healthy to FCMD, to CMM, and then to death [hazard ratios (95 % confidence intervals) per interquartile range (IQR) increment: 1.62 (1.60, 1.64) and 1.68 (1.61, 1.76) for transitions from healthy to FCMD and from FCMD to CMM, and 1.62 (1.59, 1.66), 1.67 (1.61, 1.73), and 1.52 (1.38, 1.67) for death risk from healthy, FCMD, and CMM, respectively]. After dividing FCMDs into three specific CMDs, we found that ambient air pollution had differential impacts on disease-specific transitions within the same transition phase. CONCLUSIONS Our findings indicate that there is potential for air pollution mitigation in contributing to the prevention of the development and progression of CMDs.
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Affiliation(s)
- Hongtao Zou
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Miao Cai
- 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, Saint Louis, MO 63104, USA
| | - Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Lan Chen
- 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
| | - Lauren D Arnold
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Steven W Howard
- Department of Health Management and Policy, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Haitao Li
- Department of Social Medicine and Health Service Management, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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12
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Wu Y, Zhang S, Qian SE, Cai M, Li H, Wang C, Zou H, Chen L, Vaughn MG, McMillin SE, Lin H. Ambient air pollution associated with incidence and dynamic progression of type 2 diabetes: a trajectory analysis of a population-based cohort. BMC Med 2022; 20:375. [PMID: 36310158 PMCID: PMC9620670 DOI: 10.1186/s12916-022-02573-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/21/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Though the association between air pollution and incident type 2 diabetes (T2D) has been well documented, evidence on the association with development of subsequent diabetes complications and post-diabetes mortality is scarce. We investigate whether air pollution is associated with different progressions and outcomes of T2D. METHODS Based on the UK Biobank, 398,993 participants free of diabetes and diabetes-related events at recruitment were included in this analysis. Exposures to particulate matter with a diameter ≤ 10 μm (PM10), PM2.5, nitrogen oxides (NOx), and NO2 for each transition stage were estimated at each participant's residential addresses using data from the UK's Department for Environment, Food and Rural Affairs. The outcomes were incident T2D, diabetes complications (diabetic kidney disease, diabetic eye disease, diabetic neuropathy disease, peripheral vascular disease, cardiovascular events, and metabolic events), all-cause mortality, and cause-specific mortality. Multi-state model was used to analyze the impact of air pollution on different progressions of T2D. Cumulative transition probabilities of different stages of T2D under different air pollution levels were estimated. RESULTS During the 12-year follow-up, 13,393 incident T2D patients were identified, of whom, 3791 developed diabetes complications and 1335 died. We observed that air pollution was associated with different progression stages of T2D with different magnitudes. In a multivariate model, the hazard ratios [95% confidence interval (CI)] per interquartile range elevation in PM2.5 were 1.63 (1.59, 1.67) and 1.08 (1.03, 1.13) for transitions from healthy to T2D and from T2D to complications, and 1.50 (1.47, 1.53), 1.49 (1.36, 1.64), and 1.54 (1.35, 1.76) for mortality risk from baseline, T2D, and diabetes complications, respectively. Generally, we observed stronger estimates of four air pollutants on transition from baseline to incident T2D than those on other transitions. Moreover, we found significant associations between four air pollutants and mortality risk due to cancer and cardiovascular diseases from T2D or diabetes complications. The cumulative transition probability was generally higher among those with higher levels of air pollution exposure. CONCLUSIONS This study indicates that ambient air pollution exposure may contribute to increased risk of incidence and progressions of T2D, but to diverse extents for different progressions.
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Affiliation(s)
- Yinglin Wu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Samantha E Qian
- College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63104, USA
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Haitao Li
- Department of Social Medicine and Health Service Management, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Hongtao Zou
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Michael G Vaughn
- School of Social Work, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63103, USA
| | - Stephen Edward McMillin
- School of Social Work, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63103, USA
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
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