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Tao C, Liu Z, Fan Y, Yuan Y, Wang X, Qiao Z, Li Z, Xu Q, Lou Z, Wang H, Li X, Li R, Lu C. Estimating neighborhood-based mortality risk associated with air pollution: A prospective study. JOURNAL OF HAZARDOUS MATERIALS 2024; 475:134861. [PMID: 38870855 DOI: 10.1016/j.jhazmat.2024.134861] [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: 04/08/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 06/15/2024]
Abstract
Effect modification of integrated neighborhood environment on associations of air pollution with mortality remained unclear. We analyzed data from UK biobank prospective study (n = 421,650, median 12.5 years follow-up) to examine disparities of mortality risk associated with air pollution among varied neighborhood settings. Fine particulate matter (PM2.5), PM10 and nitrogen dioxide (NO2) were measured and assigned to each participants' address. Diverse ecological and societal settings of neighborhoods were integrated with principal component analysis and categorized into disadvantaged, intermediate and advantaged levels. We estimated mortality risk associated with air pollution across diverse neighborhoods using Cox regression. We calculated community-level proportions of mortality attributable to air pollutants. There was evidence of higher all-cause and respiratory disease mortality risk associated with PM2.5 and NO2 among those in disadvantaged neighborhoods. In disadvantaged communities, air pollutants explained larger proportions of deaths and such disparities persisted over past decades. Across 2010-2021, reducing PM2.5 and NO2 to 10 μg/m3 (World Health Organization limits) would save 87,000 (52,000-120,000) and 91,000 (37,000-145,000) deaths of populations aged ≥ 40 years, with 150 000 deaths occurred in disadvantaged neighborhood settings. These findings suggested that disadvantaged neighborhoods can exacerbate mortality risk associated with air pollution.
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Affiliation(s)
- Chengzhe Tao
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Zhaoyin Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yun Fan
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yiting Yuan
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xinru Wang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Ziyan Qiao
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Zhi Li
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qiaoqiao Xu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Zhe Lou
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Haowei Wang
- School of Public Health, Imperial College London, UK; MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK
| | - Xiang Li
- School of Public Health, Imperial College London, UK; MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK
| | - Ruiyun Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
| | - Chuncheng Lu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
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Bade KJ, Mueller KT, Sparks JA. Air Pollution and Rheumatoid Arthritis Risk and Progression: Implications for the Mucosal Origins Hypothesis and Climate Change for RA Pathogenesis. Curr Rheumatol Rep 2024:10.1007/s11926-024-01160-x. [PMID: 39093508 DOI: 10.1007/s11926-024-01160-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/17/2024] [Indexed: 08/04/2024]
Abstract
PURPOSE OF REVIEW The goal of this review paper is to summarize the main research and findings regarding air pollution and its association with the risk and progression of rheumatoid arthritis (RA). RECENT FINDINGS The most studied components of air pollution included particulate matter of ≤ 2.5 microns in diameter (PM2.5), PM10, carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NOx), sulfur dioxide (SO2), and ozone (O3). In addition, specific occupations and occupational inhalants have been investigated for RA risk. Several studies showed that increased exposure to air pollutants increased the risk of developing RA, particularly seropositive RA. There was evidence of gene-inhalant interactions for seropositive RA risk. Fewer studies have been conducted on RA disease activity and bone erosions. Some studies suggest that patients with RA-associated interstitial lung disease may have worse outcomes if exposed to air pollution. We summarized associations between air pollution and increased RA risk, including RA-associated interstitial lung disease. Relatively few studies investigated air pollution and RA disease activity or other outcomes. These results suggest an important role of air pollution for seropositive RA development and suggest that climate change could be a driver in increasing RA incidence as air pollution increases.
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Affiliation(s)
- Katarina J Bade
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, #6016U, Boston, MA, 02115, USA
| | - Kevin T Mueller
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, #6016U, Boston, MA, 02115, USA
| | - Jeffrey A Sparks
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, #6016U, Boston, MA, 02115, USA.
- Harvard Medical School, Boston, MA, USA.
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Sun X, Ma S, Guo Y, Chen C, Pan L, Cui Y, Chen Z, Dijkhuizen RM, Zhou Y, Boltze J, Yu Z, Li P. The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 281:116638. [PMID: 38944013 DOI: 10.1016/j.ecoenv.2024.116638] [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: 04/09/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/01/2024]
Abstract
Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40-69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5-10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1-T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4-28.7) μg/m3; NOx, 41.3 (36.2-46.7) μg/m3; PM10, 15.9 (15.4-16.4) μg/m3; PM2.5, 9.9 (9.5-10.3) μg/m3; PM2.5 absorbance, 1.1 (1.0-1.2) per metre; and PM2.5-10, 6.1 (5.9-6.3) μg/m3. Compared with the low group, the high group's APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5-10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5-10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants.
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Affiliation(s)
- Xiaowei Sun
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Shiyang Ma
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yunlu Guo
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Caiyang Chen
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lijun Pan
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yidan Cui
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zengai Chen
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Johannes Boltze
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Zhangsheng Yu
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Peiying Li
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Outcomes Research Consortium, Cleveland, OH, United States.
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Santiago-Lamelas L, Dos Santos-Sobrín R, Carracedo Á, Castro-Santos P, Díaz-Peña R. Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases. Best Pract Res Clin Rheumatol 2024:101973. [PMID: 38997822 DOI: 10.1016/j.berh.2024.101973] [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: 05/07/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/14/2024]
Abstract
Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.
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Affiliation(s)
- Lucía Santiago-Lamelas
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Raquel Dos Santos-Sobrín
- Reumatología, Hospital Clínico Universitario, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Ángel Carracedo
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Grupo de Medicina Xenómica, CIMUS, Universidade de Santiago de Compostela, Santiago de Compostela, Spain; Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Patricia Castro-Santos
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile.
| | - Roberto Díaz-Peña
- Fundación Pública Galega de Medicina Xenómica (SERGAS), Centro Nacional de Genotipado, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile.
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Sigaux J, Semerano L, Boissier MC. Integrating social and economic status in rheumatoid arthritis exposure studies. Joint Bone Spine 2024; 91:105677. [PMID: 38135176 DOI: 10.1016/j.jbspin.2023.105677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 11/24/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Affiliation(s)
- Johanna Sigaux
- Li2P Inserm UMR 1125, 1, rue Chablis, 93017 Bobigny cedex, France; University Sorbonne Paris Nord, 1, rue Chablis, 93017 Bobigny cedex, France; Rheumatology Department, Avicenne Hospital (AP-HP), 125, rue de Stalingrad, 93017 Bobigny, France.
| | - Luca Semerano
- Li2P Inserm UMR 1125, 1, rue Chablis, 93017 Bobigny cedex, France; University Sorbonne Paris Nord, 1, rue Chablis, 93017 Bobigny cedex, France; Rheumatology Department, Avicenne Hospital (AP-HP), 125, rue de Stalingrad, 93017 Bobigny, France
| | - Marie-Christophe Boissier
- Li2P Inserm UMR 1125, 1, rue Chablis, 93017 Bobigny cedex, France; University Sorbonne Paris Nord, 1, rue Chablis, 93017 Bobigny cedex, France; Rheumatology Department, Avicenne Hospital (AP-HP), 125, rue de Stalingrad, 93017 Bobigny, France
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Wen J, Zhang J, Zhang H, Zhang N, Lei R, Deng Y, Cheng Q, Li H, Luo P. Large-scale genome-wide association studies reveal the genetic causal etiology between air pollutants and autoimmune diseases. J Transl Med 2024; 22:392. [PMID: 38685026 PMCID: PMC11057084 DOI: 10.1186/s12967-024-04928-y] [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: 08/14/2023] [Accepted: 01/23/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Epidemiological evidence links a close correlation between long-term exposure to air pollutants and autoimmune diseases, while the causality remained unknown. METHODS Two-sample Mendelian randomization (TSMR) was used to investigate the role of PM10, PM2.5, NO2, and NOX (N = 423,796-456,380) in 15 autoimmune diseases (N = 14,890-314,995) using data from large European GWASs including UKB, FINNGEN, IMSGC, and IPSCSG. Multivariable Mendelian randomization (MVMR) was conducted to investigate the direct effect of each air pollutant and the mediating role of common factors, including body mass index (BMI), alcohol consumption, smoking status, and household income. Transcriptome-wide association studies (TWAS), two-step MR, and colocalization analyses were performed to explore underlying mechanisms between air pollution and autoimmune diseases. RESULTS In TSMR, after correction of multiple testing, hypothyroidism was causally associated with higher exposure to NO2 [odds ratio (OR): 1.37, p = 9.08 × 10-4] and NOX [OR: 1.34, p = 2.86 × 10-3], ulcerative colitis (UC) was causally associated with higher exposure to NOX [OR: 2.24, p = 1.23 × 10-2] and PM2.5 [OR: 2.60, p = 5.96 × 10-3], rheumatoid arthritis was causally associated with higher exposure to NOX [OR: 1.72, p = 1.50 × 10-2], systemic lupus erythematosus was causally associated with higher exposure to NOX [OR: 4.92, p = 6.89 × 10-3], celiac disease was causally associated with lower exposure to NOX [OR: 0.14, p = 6.74 × 10-4] and PM2.5 [OR: 0.17, p = 3.18 × 10-3]. The risky effects of PM2.5 on UC remained significant in MVMR analyses after adjusting for other air pollutants. MVMR revealed several common mediators between air pollutants and autoimmune diseases. Transcriptional analysis identified specific gene transcripts and pathways interconnecting air pollutants and autoimmune diseases. Two-step MR revealed that POR, HSPA1B, and BRD2 might mediate from air pollutants to autoimmune diseases. POR pQTL (rs59882870, PPH4=1.00) strongly colocalized with autoimmune diseases. CONCLUSION This research underscores the necessity of rigorous air pollutant surveillance within public health studies to curb the prevalence of autoimmune diseases.
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Affiliation(s)
- Jie Wen
- The Animal Laboratory Center, Hunan Cancer Hospital, and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jingwei Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Nan Zhang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Ruoyan Lei
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Yujia Deng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- First Clinical Department, Changsha Medical University, Changsha, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
- Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
| | - He Li
- The Animal Laboratory Center, Hunan Cancer Hospital, and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
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Jiang Z, Zhang S, Gao T, Chen K, Liu Y, Liu Y, Wang T, Zeng P. Co-exposure to multiple air pollutants, genetic susceptibility, and the risk of myocardial infarction onset: a cohort analysis of the UK Biobank participants. Eur J Prev Cardiol 2024; 31:698-706. [PMID: 38085043 DOI: 10.1093/eurjpc/zwad384] [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: 10/05/2023] [Revised: 11/18/2023] [Accepted: 12/06/2023] [Indexed: 04/19/2024]
Abstract
AIMS The relationship between the long-term joint exposure to ambient air pollution and incidence of myocardial infarction (MI) and modification by genetic susceptibility remain inconclusive. METHODS AND RESULTS We analysed 329 189 UK Biobank participants without MI at baseline. Exposure concentrations to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were obtained. Air pollution score assessing the joint exposure was calculated, and its association with MI was evaluated via Cox model under the P value aggregation framework. Genetic susceptibility to MI was evaluated by incorporating polygenic risk score (PRS) into models. Risk prediction models were also established. During a median follow-up of 13.4 years, 9993 participants developed MI. Per interquartile range increase of PM2.5, PM10, NO2, and NOx resulted in 74% [95% confidence intervals (CIs) 69%-79%], 67% (63%-72%), 46% (42%-49%), and 38% (35%-41%) higher risk of MI. Compared with the lowest quartile (Q1) of air pollution score, the multivariable adjusted hazard ratio (HR) (95%CIs) of Q4 (the highest cumulative air pollution) was 3.50 (3.29-3.72) for MI. Participants with the highest PRS and air pollution score possessed the highest risk of incident MI (HR = 4.88, 95%CIs 4.35-5.47). Integrating PRS, air pollution exposure, and traditional factors substantially improved risk prediction of MI. CONCLUSION Long-term joint exposure to air pollutants including PM2.5, PM10, NO2, and NOx is substantially associated with increased risk of MI. Genetic susceptibility to MI strengthens such adverse joint association. Air pollutions together with genetic and traditional factors enhance the accuracy of MI risk prediction.
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Affiliation(s)
- Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
| | - Tongyu Gao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
| | - Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
| | - Yuxin Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
| | - Ying Liu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China
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Moore JM, Norris JM, Clark ML. Exposure to air pollutants and rheumatoid arthritis biomarkers: A scoping review. Semin Arthritis Rheum 2024; 65:152365. [PMID: 38232624 DOI: 10.1016/j.semarthrit.2024.152365] [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: 10/05/2023] [Revised: 12/20/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION Rheumatoid arthritis (RA) is a common autoimmune disease with a complex and poorly understood etiology that includes genetic, hormonal, and environmental factors. OBJECTIVE Our objective was to assess current literature that investigated the association between exposure to environmental and occupational air pollutants and RA-related biomarkers rheumatoid factor (RF) and anti-citrullinated peptide antibody (ACPA). DESIGN PubMed and Web of Science were used to identify epidemiological studies that measured or estimated air pollution and at least one RA biomarker. Information was charted for comparison of evidence, including pollutant(s) studied, exposure assessment, biomarker measurement, analysis method, study population, size, dates, adjustment variables, and findings. RESULTS Several common air pollutants (including two mixtures) and a few dozen occupational inhalants were assessed in 13 eligible studies. Associations between industrial sulfur dioxide and particulate matter less than 2.5 µm in diameter with ACPA were observed most frequently, including associations between residential proximity to pollution sources and ACPA positivity. Consistency of associations with other pollutants was either not observed or limited to single studies. Three studies evaluated the modifying impact of SE alleles (a genetic factor associated with RA) and found that pollutant associations were stronger among participants positive for SE alleles. CONCLUSION Based on mixed results, there was no consistent link between any single pollutant and RA-related biomarker outcomes. Comparisons across studies were limited by differences in study populations (e.g., by RA status, by sociodemographic groups) and study design (including designs focused on different sources of air pollution, methodological approaches with varying levels of potential exposure misclassification, and assessments of inconsistent biomarker cut-points). However, given that multiple studies reported associations between exposure to air pollution and RA biomarkers, continued exploration utilizing studies that can be designed with a more robust causal framework, including continued consideration of effect modification by genetic status, may be necessary.
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Affiliation(s)
- Jillian M Moore
- Department of Environmental and Radiological Health Sciences, Colorado State University, 1681 Campus Delivery, Fort Collins, CO 80523, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora CO, USA
| | - Maggie L Clark
- Department of Environmental and Radiological Health Sciences, Colorado State University, 1681 Campus Delivery, Fort Collins, CO 80523, USA.
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Hu H, Yang X, Chen Q, Huang X, Cao X, Zhang X, Xu Y. Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study. Front Public Health 2024; 12:1333811. [PMID: 38605869 PMCID: PMC11007215 DOI: 10.3389/fpubh.2024.1333811] [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: 11/06/2023] [Accepted: 03/19/2024] [Indexed: 04/13/2024] Open
Abstract
Background In recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs. Methods We utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship. Results In the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89-7.88; PIVW < 0.00625), while a potential association exists between PM2.5-10 and vitiligo (OR = 7.42; 95% CI: 1.02-53.94; PIVW < 0.05), as well as between PM2.5 and systemic lupus erythematosus (OR = 68.17; 95% CI: 2.17-2.1e+03; PIVW < 0.05). In East Asian populations, no causal relationship was found between air pollutants and the risk of systemic lupus erythematosus and rheumatoid arthritis (PIVW > 0.025). There was no pleiotropy in the results. Conclusion Our results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed.
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Affiliation(s)
- Haiping Hu
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xinxin Yang
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Qingquan Chen
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xinfeng Huang
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiangyu Cao
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiaoyang Zhang
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Youqiong Xu
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
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Sun K, Jin S, Yang Z, Li X, Li C, Zhang J, Yang G, Yang C, Abdelrahman Z, Liu Z. Transition to healthier lifestyle associated with reduced risk of incident dementia and decreased hippocampal atrophy. J Affect Disord 2024; 349:552-558. [PMID: 38195008 DOI: 10.1016/j.jad.2024.01.007] [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: 08/24/2023] [Revised: 11/23/2023] [Accepted: 01/03/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND Research has estimated the associations of lifestyle at one-time point with the risk of dementia and hippocampal volume, but the impact of lifestyle transition on dementia and hippocampal volume remains unclear. This study aims to examine the associations of lifestyle transition with the risk of dementia and hippocampal volume. METHODS Based on data from the UK Biobank, a weighted lifestyle score was constructed by incorporating six lifestyle factors. Within each baseline lifestyle group (i.e., healthy, intermediate, and unhealthy), lifestyle transition was classified into decline, maintenance, and improvement. Cox proportional hazard regression was used to estimate the association of lifestyle transition and incident dementia (N = 16,305). A multiple linear regression model was used to estimate the association between lifestyle transition and hippocampal volume (N = 5849). RESULTS During a median follow-up period of 8.6 years, 120 (0.7 %) dementia events were documented. Among participants with healthy baseline lifestyles, the improvement group had a lower risk of incident dementia (HR: 0.18, 95 % CI: 0.04-0.81) and a larger hippocampal volume (β = 111.69, P = 0.026) than the decline group. Similar results were observed among participants with intermediate baseline lifestyles regarding dementia risk but not hippocampal volume. No benefits were observed in the improvement group among those with unhealthy baseline lifestyles. LIMITATIONS A lower incidence of dementia than other cohort study and this may have resulted in an underestimation of the risk of dementia. CONCLUSIONS Earlier transitions to healthier lifestyle were associated with reduced risk of incident dementia and decreased hippocampal atrophy.
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Affiliation(s)
- Kaili Sun
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Shuyi Jin
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhenqing Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chenxi Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chongming Yang
- Research Support Center, Brigham Young University, Provo, UT 84602, USA
| | - Zeinab Abdelrahman
- Centre for Public Health, Queen's University of Belfast, Belfast BT12 6BA, UK
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China.
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11
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Cheng B, Pan C, Cai Q, Liu L, Cheng S, Yang X, Meng P, Wei W, He D, Liu H, Jia Y, Wen Y, Xu P, Zhang F. Long-term ambient air pollution and the risk of musculoskeletal diseases: A prospective cohort study. JOURNAL OF HAZARDOUS MATERIALS 2024; 466:133658. [PMID: 38310839 DOI: 10.1016/j.jhazmat.2024.133658] [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: 09/29/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/06/2024]
Abstract
Evidence of the associations of air pollution and musculoskeletal diseases is inconsistent. This study aimed to examine the associations between air pollutants and the risk of incident musculoskeletal diseases, such as degenerative joint diseases (n = 38,850) and inflammatory arthropathies (n = 20,108). An air pollution score was constructed to assess the combined effect of PM2.5, PM2.5-10, NO2, and NOX. Cox proportional hazard model was applied to assess the relationships between air pollutants and the incidence of each musculoskeletal disease. The air pollution scores exhibited the modest association with an increased risk of osteoporosis (HR = 1.006, 95% CI: 1.002-1.011). Among the individual air pollutants, PM2.5 and PM2.5-10 exhibited the most significant effect on elevated risk of musculoskeletal diseases, such as PM2.5 on osteoporosis (HR = 1.064, 95% CI: 1.020-1.110), PM2.5-10 on inflammatory arthropathies (HR = 1.059, 95% CI: 1.037-1.081). Females were found to have a higher risk of incident musculoskeletal diseases when exposed to air pollutants. Individuals with extreme BMI or lower socioeconomic status had a higher risk of developing musculoskeletal diseases. Our findings reveal that long-term exposure to ambient air pollutants may contribute to an increased risk of musculoskeletal diseases.
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Affiliation(s)
- Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China
| | - Peng Xu
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China.
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an 710061, China; Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an 710061, China; Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, 710061, China.
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Li P, Wang Y, Tian D, Liu M, Zhu X, Wang Y, Huang C, Bai Y, Wu Y, Wei W, Tian S, Li Y, Qiao Y, Yang J, Cao S, Cong C, Zhao L, Su J, Wang M. Joint Exposure to Ambient Air Pollutants, Genetic Risk, and Ischemic Stroke: A Prospective Analysis in UK Biobank. Stroke 2024; 55:660-669. [PMID: 38299341 DOI: 10.1161/strokeaha.123.044935] [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: 08/29/2023] [Accepted: 12/20/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Our primary objective was to assess the association between joint exposure to various air pollutants and the risk of ischemic stroke (IS) and the modification of the genetic susceptibility. METHODS This observational cohort study included 307 304 British participants from the United Kingdom Biobank, who were stroke-free and possessed comprehensive baseline data on genetics, air pollutant exposure, alcohol consumption, and dietary habits. All participants were initially enrolled between 2006 and 2010 and were followed up until 2022. An air pollution score was calculated to assess joint exposure to 5 ambient air pollutants, namely particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, as well as nitrogen oxide and nitrogen dioxide. To evaluate individual genetic risk, a polygenic risk score for IS was calculated for each participant. We adjusted for demographic, social, economic, and health covariates. Cox regression models were utilized to estimate the associations between air pollution exposure, polygenic risk score, and the incidence of IS. RESULTS Over a median follow-up duration of 13.67 years, a total of 2476 initial IS events were detected. The hazard ratios (95% CI) of IS for per 10 µg/m3 increase in particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, nitrogen dioxide, and nitrogen oxide were 1.73 (1.33-2.14), 1.24 (0.88-1.70), 1.13 (0.89-1.33), 1.03 (0.98-1.08), and 1.04 (1.02-1.07), respectively. Furthermore, individuals in the highest quintile of the air pollution score exhibited a 29% to 66% higher risk of IS compared with those in the lowest quintile. Notably, participants with both high polygenic risk score and air pollution score had a 131% (95% CI, 85%-189%) greater risk of IS than participants with low polygenic risk score and air pollution score. CONCLUSIONS Our findings suggested that prolonged joint exposure to air pollutants may contribute to an increased risk of IS, particularly among individuals with elevated genetic susceptibility to IS.
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Affiliation(s)
- Panlong Li
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Ying Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China (Ying Wang)
- School of Public Health, Zhengzhou University (Ying Wang)
| | - Dandan Tian
- Department of Hypertension (D.T., M.L.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Min Liu
- Department of Hypertension (D.T., M.L.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Xirui Zhu
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Yanfeng Wang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Chun Huang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Yan Bai
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Science, China (Y.B.)
| | - Yaping Wu
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Wei Wei
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Shan Tian
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Yuna Li
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Yuan Qiao
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Junting Yang
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Shanshan Cao
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Chaohua Cong
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Lei Zhao
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Jingjing Su
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Meiyun Wang
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
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Dong Y, Ding W, Song K, Li F. Higher Risk of Rheumatoid Arthritis in Patients With Chronic Rhinosinusitis: Prospective Association in the U.K. Biobank and Genetic Evidence by Mendelian Randomization Analysis. Am J Rhinol Allergy 2024; 38:82-91. [PMID: 38225197 DOI: 10.1177/19458924231225488] [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] [Indexed: 01/17/2024]
Abstract
BACKGROUND Previous studies have shown that respiratory diseases are associated with an increased risk of rheumatoid arthritis (RA). However, whether there is a correlation between chronic rhinosinusitis (CRS) and RA is not known. Due to the high incidence of CRS, it remains to be clarified whether we should pay additional attention to RA risk in the huge population of CRS. METHODS We used a 2-sample Mendelian randomization (MR) analysis to explore the causal effects of CRS on the incidence of RA. The inverse variance weighted (IVW) approach was used as the main analysis in the MR randomization study. Then, we used the data from the U.K. Biobank to examine the association between RA and CRS at the individual level in a prospective cohort. We identified patients with CRS at the time of recruitment and further followed the incidence of RA until 2021. The risk of developing RA in patients with CRS was determined by a multivariate Cox regression model. We used 3 multivariate Cox models to adjust for individual characteristics, lifestyle factors and concomitant diseases, respectively. RESULTS The MR analysis by the IVW model suggested that the odds ratio of RA associated with genetically predicted CRS was 2.39 (95% CI [1.08-5.30]; p = .032). In the first multivariate model adjusting for individual characteristics, CRS was associated with a 47% increase of risk of developing RA (hazard ratio [HR] = 1.47; 95% CI [1.12-1.90]). In the second multivariate model adjusting for lifestyle factors, the HR of RA associated with CRS was 1.48 (95% CI [1.15-1.90]). In the third multivariate model, chronic sinusitis was associated with a 32% increase in RA risk (HR = 1.32; 95% CI [1.03-1.70]). CONCLUSION CRS has a genetically causal effect on the incidence of RA, and the risk of RA is greatly higher in CRS at the individual level. This is the first study to reveal an association between CRS and RA. Due to the high incidence of CRS, it is recommended that additional attention should be paid to the increased RA risk in patients with CRS compared to that in common people.
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Affiliation(s)
- Yimin Dong
- Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weizhong Ding
- Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kehan Song
- Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Li
- Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Zhang S, Cao H, Chen K, Gao T, Zhao H, Zheng C, Wang T, Zeng P, Wang K. Joint Exposure to Multiple Air Pollutants, Genetic Susceptibility, and Incident Dementia: A Prospective Analysis in the UK Biobank Cohort. Int J Public Health 2024; 69:1606868. [PMID: 38426188 PMCID: PMC10901982 DOI: 10.3389/ijph.2024.1606868] [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: 11/19/2023] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
Objectives: This study aimed to evaluate the joint effects of multiple air pollutants including PM2.5, PM10, NO2, and NOx with dementia and examined the modifying effects of genetic susceptibility. Methods: This study included 220,963 UK Biobank participants without dementia at baseline. Weighted air pollution score reflecting the joint exposure to multiple air pollutants were constructed by cross-validation analyses, and inverse-variance weighted meta-analyses were performed to create a pooled effect. The modifying effect of genetic susceptibility on air pollution score was assessed by genetic risk score and APOE ε4 genotype. Results: The HR (95% CI) of dementia for per interquartile range increase of air pollution score was 1.13 (1.07∼1.18). Compared with the lowest quartile (Q1) of air pollution score, the HR (95% CI) of Q4 was 1.26 (1.13∼1.40) (P trend = 2.17 × 10-5). Participants with high air pollution score and high genetic susceptibility had higher risk of dementia compared to those with low air pollution score and low genetic susceptibility. Conclusion: Our study provides evidence that joint exposure to multiple air pollutants substantially increases the risk of dementia, especially among individuals with high genetic susceptibility.
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Affiliation(s)
- Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Hongyan Cao
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Tongyu Gao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Huashuo Zhao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Chu Zheng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ke Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China
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15
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Huang YM, Ma YH, Gao PY, Cui XH, Hou JH, Chi HC, Fu Y, Wang ZB, Feng JF, Cheng W, Tan L, Yu JT. Genetic susceptibility modifies the association of long-term air pollution exposure on Parkinson's disease. NPJ Parkinsons Dis 2024; 10:23. [PMID: 38233432 PMCID: PMC10794179 DOI: 10.1038/s41531-024-00633-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
Inconsistent findings exist regarding the potential association between polluted air and Parkinson's disease (PD), with unclear insights into the role of inherited sensitivity. This study sought to explore the potential link between various air pollutants and PD risk, investigating whether genetic susceptibility modulates these associations. The population-based study involved 312,009 initially PD-free participants with complete genotyping data. Annual mean concentrations of PM2.5, PM10, NO2, and NOx were estimated, and a polygenic risk score (PRS) was computed to assess individual genetic risks for PD. Cox proportional risk models were employed to calculate hazard ratios (HR) and 95% confidence intervals (CI) for the associations between ambient air pollutants, genetic risk, and incident PD. Over a median 12.07-year follow-up, 2356 PD cases (0.76%) were observed. Compared to the lowest quartile of air pollution, the highest quartiles of NO2 and PM10 pollution showed HRs and 95% CIs of 1.247 (1.089-1.427) and 1.201 (1.052-1.373) for PD incidence, respectively. Each 10 μg/m3 increase in NO2 and PM10 yielded elevated HRs and 95% CIs for PD of 1.089 (1.026-1.155) and 1.363 (1.043-1.782), respectively. Individuals with significant genetic and PM10 exposure risks had the highest PD development risk (HR: 2.748, 95% CI: 2.145-3.520). Similarly, those with substantial genetic and NO2 exposure risks were over twice as likely to develop PD compared to minimal-risk counterparts (HR: 2.414, 95% CI: 1.912-3.048). Findings suggest that exposure to air contaminants heightens PD risk, particularly in individuals genetically predisposed to high susceptibility.
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Affiliation(s)
- Yi-Ming Huang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Hui Ma
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Pei-Yang Gao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Xi-Han Cui
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Jia-Hui Hou
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Changchun Street 45, Beijing, China
| | - Hao-Chen Chi
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Zhi-Bo Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200040, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, 321004, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200032, China
- Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, 200040, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, 321004, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
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16
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Chen L, Wu B, Mo L, Chen H, Zhao Y, Tan T, Chen L, Li Y, Yao P, Tang Y. Associations between biological ageing and the risk of, genetic susceptibility to, and life expectancy associated with rheumatoid arthritis: a secondary analysis of two observational studies. THE LANCET. HEALTHY LONGEVITY 2024; 5:e45-e55. [PMID: 38081205 DOI: 10.1016/s2666-7568(23)00220-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND Rheumatoid arthritis is a chronic autoimmune disorder that affects life expectancy. Accelerated biological ageing is thought to be a major risk factor for age-related diseases, but its role in rheumatoid arthritis remains uncertain. We aimed to assess the associations between biological ageing and risk of rheumatoid arthritis and genetic susceptibility to the disease. We also aimed to assess the effect of biological ageing on the life expectancy of people with rheumatoid arthritis. METHODS We calculated the chronological age-adjusted biological age-by both the Klemera-Doubal method (KDMAge) and phenotypic age (PhenoAge)-as a surrogate measure for biological ageing in participants from the US National Health and Nutrition Examination Survey (NHANES) and UK Biobank study. KDMAge or PhenoAge acceleration was defined as the residual of the regression of KDMAge or PhenoAge based on chronological age. Participants with accelerated biological ageing had KDMAge or PhenoAge acceleration values greater than 0, whereas those without accelerated ageing had values less than or equal to 0. We did cross-sectional analyses to assess the association between biological ageing and prevalent rheumatoid arthritis in both cohorts and prospective analyses to assess the association between biological ageing and incident rheumatoid arthritis in the UK Biobank. Logistic regression and Cox proportional hazards models were used to analyse these associations. Polygenic risk scores were used to establish genetic susceptibility to rheumatoid arthritis and to analyse the interaction between biological ageing and genetic risk. We also assessed the association between life expectancy and biological ageing status in people with rheumatoid arthritis. FINDINGS In the cross-sectional analyses, each 1-year increase in age-adjusted biological age was associated with an increase in the risk of rheumatoid arthritis of between 1% and 10%. In the NHANES, individuals with accelerated ageing had a higher risk of rheumatoid arthritis than non-accelerated ageing individuals, with odds ratios of 1·21 (95% CI 1·03-1·42; p=0·018) for KDMAge acceleration and 1·46 (1·26-1·69; p<0·0001) for PhenoAge acceleration. Similarly, in the UK Biobank, the risk of rheumatoid arthritis was increased in individuals with accelerated ageing compared with individuals with no accelerated ageing (KDMAge odds ratio 1·96 [95% CI 1·71-2·24]; PhenoAge 2·71 [2·51-2·92]). In the prospective analyses of the UK Biobank population, accelerated biological ageing was associated with an increased risk of incident rheumatoid arthritis as measured by both KDMAge (hazard ratio 1·27 [95% CI 1·03-1·55]) and PhenoAge (1·70 [1·52-1·92]). Among participants with high genetic predisposition to rheumatoid arthritis, accelerated biological ageing was associated with an increased risk of incident disease, and we noted significant additive interactions between accelerated biological ageing and genetic risk. At age 45 years, people with rheumatoid arthritis had reduced life expectancy compared with those without rheumatoid arthritis. Among people with rheumatoid arthritis, accelerated biological ageing was associated with reduced life expectancy compared with not having accelerated biological ageing. INTERPRETATION Accelerated biological ageing could increase the risk of rheumatoid arthritis, especially among people with high genetic risk, and could reduce the life expectancy of people with rheumatoid arthritis. The identification of populations with accelerated biological ageing has important implications for reducing the risk of rheumatoid arthritis and of lowered life expectancy. FUNDING National Natural Science Foundation of China.
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Affiliation(s)
- Li Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bangfu Wu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Mo
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huimin Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Zhao
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tianqi Tan
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanyan Li
- Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Ping Yao
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuhan Tang
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health and MOE Key Lab of Environment and Health, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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17
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Lin J, Zhang Y, Wang K, Xia H, Hua M, Lu K, Zheng W, Chen R. Long-term impact of PM 2.5 exposure on frailty, chronic diseases, and multimorbidity among middle-aged and older adults: insights from a national population-based longitudinal study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:4100-4110. [PMID: 38097844 DOI: 10.1007/s11356-023-31505-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/08/2023] [Indexed: 01/19/2024]
Abstract
Particulate Matter 2.5 (PM2.5) is a significant risk factor for frailty and chronic diseases. Studies on the associations between PM2.5 and frailty, chronic diseases, and multimorbidity are scarce, especially from large cohort studies. We aimed to explore the potential association between PM2.5 exposure and the risk of frailty, chronic diseases, and multimorbidity. We collected data from a national cohort (CHARLS) with a follow-up period of 11-18 years, totaling 13,366 participants. We obtained PM2.5 concentration data from the Atmospheric Composition Analysis Group at Dalhousie University. PM2.5 exposure is based on the average annual concentration in the prefecture-level city where residents live. We define frailty as the comprehensive manifestation of declining various body functions, characterized by a frailty index of 0.25 or greater, and multimorbidity as the presence of at least two or more chronic conditions. Cox proportional hazards regression was used to estimate the hazard ratio (HR) with its 95% confidence interval (95%CI). A 10-μg/m3 increase for PM2.5 was significantly associated with an increased risk of frailty (HR = 1.289, 95%CI = 1.257-1.322, P < 0.001). A 10-μg/m3 increase for PM2.5 was significantly associated with the elevated risk for most chronic diseases. Compared to those with no morbidity or only single morbidity, a 10-μg/m3 increase for PM2.5 was significantly associated with the elevated risk for multimorbidity (HR = 1.220, 95%CI = 1.181-1.260, P < 0.001). Ambient PM2.5 exposure is a significant risk factor for frailty, chronic diseases, and multimorbidity, and some measures need to be taken to reduce PM2.5 concentration and prevent frailty and chronic diseases.
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Affiliation(s)
- Junjie Lin
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yu Zhang
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Kunyi Wang
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Huilin Xia
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Minxia Hua
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Kexin Lu
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Weijun Zheng
- Department of Medical Statistics, School of Public Health, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou City, 310053, Zhejiang Province, China
| | - Rucheng Chen
- Department of Medical Statistics, School of Public Health, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou City, 310053, Zhejiang Province, China.
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18
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Kronzer VL, Hayashi K, Crowson CS, Davis JM, McDermott GC, Cui J, Losina E, Juge PA, Cerhan JR, Sparks JA. Gene-respiratory disease interactions for rheumatoid arthritis risk. Semin Arthritis Rheum 2023; 63:152254. [PMID: 37595508 PMCID: PMC10840753 DOI: 10.1016/j.semarthrit.2023.152254] [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: 04/19/2023] [Revised: 06/30/2023] [Accepted: 08/07/2023] [Indexed: 08/20/2023]
Abstract
OBJECTIVE We aimed to identify gene by respiratory tract disease interactions that increase RA risk. METHODS In this case-control study using the Mass General Brigham Biobank, we matched incident RA cases, confirmed by ACR/EULAR criteria, to four controls on age, sex, and electronic health record history. Genetic exposures included a validated overall genetic risk score (GRS) for RA, a Human Leukocyte Antigen (HLA) GRS for RA, and the MUC5B promoter variant, an established risk factor for RA-associated interstitial lung disease (ILD). Preceding respiratory tract diseases came from diagnosis codes (positive predictive value 86%). We estimated attributable proportions (AP) and multiplicative odds ratios (OR) with 95% confidence intervals (CI) for RA for each genetic and respiratory exposure using conditional logistic regression models, adjusting for potential confounders. RESULTS We identified 653 incident RA cases and 2,607 matched controls (mean 54 years, 76% female). The highest tertile of the overall GRS and the HLA GRS were both associated with increased RA risk (OR 2.28, 95% CI 1.89,2.74; OR 2.02, 95% CI 1.67-2.45). ILD and the HLA GRS exhibited a synergistic relationship for RA risk (OR for both exposures 4.30, 95% CI 1.28,14.38; AP 0.51, 95% CI-0.16,1.18). Asthma and the MUC5B promoter variant also exhibited a synergistic interaction for seropositive RA (OR for both exposures 2.58, 95% CI 1.10,6.07; AP 0.62, 95% CI 0.24,1.00). CONCLUSION ILD-HLA GRS and asthma-MUC5B promoter variant showed synergistic interactions for RA risk. Such interactions may prove useful for RA prevention and screening.
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Affiliation(s)
| | - Keigo Hayashi
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital; Harvard Medical School, Boston, USA.
| | - Cynthia S Crowson
- Division of Rheumatology, Mayo Clinic, Rochester, MN, USA; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
| | - John M Davis
- Division of Rheumatology, Mayo Clinic, Rochester, MN, USA.
| | - Gregory C McDermott
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital; Harvard Medical School, Boston, USA.
| | - Jing Cui
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Elena Losina
- Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, USA.
| | - Pierre-Antoine Juge
- Dept of Rheumatology, DMU Locomotion, INSERM UMR1152, Hôpital Bichat-Claude Bernard, APHP, Université de Paris, Paris, France.
| | - James R Cerhan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
| | - Jeffrey A Sparks
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital; Harvard Medical School, Boston, USA.
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19
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Vaskimo LM, Gomon G, Naamane N, Cordell HJ, Pratt A, Knevel R. The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective. Genes (Basel) 2023; 14:2167. [PMID: 38136989 PMCID: PMC10743278 DOI: 10.3390/genes14122167] [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: 11/01/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.
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Affiliation(s)
- Lotta M. Vaskimo
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Georgy Gomon
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Najib Naamane
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Heather J. Cordell
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Arthur Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Department of Rheumatology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
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20
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Maisha JA, El-Gabalawy HS, O’Neil LJ. Modifiable risk factors linked to the development of rheumatoid arthritis: evidence, immunological mechanisms and prevention. Front Immunol 2023; 14:1221125. [PMID: 37767100 PMCID: PMC10520718 DOI: 10.3389/fimmu.2023.1221125] [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: 05/11/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Rheumatoid Arthritis (RA) is a common autoimmune disease that targets the synovial joints leading to arthritis. Although the etiology of RA remains largely unknown, it is clear that numerous modifiable risk factors confer increased risk to developing RA. Of these risk factors, cigarette smoking, nutrition, obesity, occupational exposures and periodontal disease all incrementally increase RA risk. However, the precise immunological mechanisms by which these risk factors lead to RA are not well understood. Basic and translational studies have provided key insights into the relationship between inflammation, antibody production and the influence in other key cellular events such as T cell polarization in RA risk. Improving our general understanding of the mechanisms which lead to RA will help identify targets for prevention trials, which are underway in at-risk populations. Herein, we review the modifiable risk factors that are linked to RA development and describe immune mechanisms that may be involved. We highlight the few studies that have sought to understand if modification of these risk factors reduces RA risk. Finally, we speculate that modification of risk factors may be an appealing avenue for prevention for some at-risk individuals, specifically those who prefer lifestyle interventions due to safety and economic reasons.
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Affiliation(s)
| | | | - Liam J. O’Neil
- Manitoba Centre for Proteomics and Systems Biology, Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
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21
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McDermott GC, Sparks JA. Invited Perspective: Air Pollutants, Genetics, and the Mucosal Paradigm for Rheumatoid Arthritis Risk. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:31303. [PMID: 36913236 PMCID: PMC10010382 DOI: 10.1289/ehp12167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/08/2022] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Gregory C. McDermott
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey A. Sparks
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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