Relationship between environmental pollution and mitochondrial DNA copy number in European and East Asian populations: a Mendelian randomization study

Relationship between environmental pollution and mitochondrial DNA copy number in European and East Asian populations: a Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Relationship between environmental pollution and mitochondrial DNA copy number in European and East Asian populations: a Mendelian randomization study Binbin Zhang, Bin Dou, Chuan Lu, Qi Yan, Dawa Zhaxi, Shuqing Wei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4506104/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND In recent years, the incidence of diseases associated with environmental pollution has increased dramatically worldwide. Previous studies have shown that mitochondrial DNA (mtDNA) copy number is a potential biomarker for diseases caused by environmental pollution, and therefore, the causal relationship between environmental pollution and mtDNA copy number needs to be further explored. METHODS We performed Mendelian randomization analyses of European and Asian populations using a large amount of publicly available genome-wide association study (GWAS) pooled data. Genetic loci that are independent of each other and strongly associated with environmental pollution were selected as instrumental variables, and the inverse variance weighting (IVW) method was used as the primary analytical method. Cochrane's Q-test was used to assess heterogeneity. Multiplicity was checked using MR-Egger regression test.MR-PRESSO method was used to identify outliers. Sensitivity analysis was performed using leave-one-out. The results were assessed based on effect indicator dominance ratio (OR) and 95% confidence interval (CI). RESULTS In the European population, genetically predicted PM2.5 (p = 0.341), PM2.5-10 (p = 0.954), PM10 (p = 0.710), nitrogen dioxide (p = 0.196), nitrogen oxides (p = 0.524), workplace full of chemical or other fumes (p = 0.194), workplace with a lot of cigarette smoke from other people smoking (p = 0.847), workplace had a lot of diesel exhaust (p = 0.677), workplace very cold (p = 0.541), workplace very cold (p = 0.778), workplace very hot (p = 0.554), and workplace very noisy (p = 0.973) were not associated with risk of mtDNA copy number. In the Asian population, genetically predicted PM2.5 (p = 0.990), PM2.5-10 (p = 0.739), PM10 (p = 0.537), nitrogen dioxide (p = 0.341), and nitrogen oxides (p = 0.735) were not associated with the risk of mtDNA copy number. Sensitivity analysis proved the stability of the results. CONCLUSION The results of this Mendelian randomization do not support a causal relationship between environmental pollution and mtDNA copy number. However, the causal relationship found in this study still needs to be further explored. Health sciences/Medical research/Genetics research Health sciences/Health care/Public health Environmental pollution Air pollution Mitochondrial DNA copy number Mendelian randomization GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Rapid economic and social development has introduced new health risks [ 1 – 2 ]. Among these risks, atmospheric pollution has emerged as a critical global public health concern [ 3 – 4 ]. Notably, atmospheric particulate matter (PM) is identified as the most chemically complex and hazardous pollutant in the atmosphere [ 5 ]. A 25-year study revealed that air pollution significantly impacts the global burden of disease, with PM2.5 ranking as the fifth risk factor for mortality in 2015 [ 6 ]. Moreover, exposure to diverse work environments can lead to various occupational diseases among workers [ 7 – 9 ]. Identifying key disease-causing environmental factors from numerous risk factors poses a significant challenge for environmental health research. Mitochondrial DNA (mtDNA) is the sole genome present in animal cell organelles, responsible for controlling and coding certain proteins, with its quantity in the genome termed as mtDNA copy number [ 10 ]. Variations in mtDNA copy number have been linked to metabolic syndrome, neurodegenerative diseases, cardiovascular diseases, and cancer. The high specificity of mtDNA copy number is making it a progressively utilized disease biomarker [ 11 – 12 ]. In contrast to the nuclear genome, mtDNA lacks an efficient damage repair system, making it more susceptible to environmental, genetic, and other influences, resulting in alterations in replication, transcription levels, and copy number [ 13 ]. However, the precise mechanisms underlying these changes remain unclear, and studying mtDNA copy number stands as a crucial pathway to furthering our understanding of mitochondria and disease progression. In recent years, there has been growing evidence linking exposure to Particulate Matter (PM) in the atmosphere with changes in mtDNA copy number [ 14 ]. Environmental pollutants have been shown to trigger mitochondrial toxicity, resulting in oxidative stress, disruption of mitochondrial ATP production, and interference with mitochondria-mediated cell signaling [ 15 ]. However, existing studies have produced conflicting results. For instance, a study involving 2758 women reported that prolonged exposure to PM2.5 decreased leukocyte mtDNA copy number [ 16 ], while another study suggested that long-term air pollution exposure actually increased mtDNA copy number in the body [ 17 ]. Similarly, research on air pollution and placental mtDNA copy number has yielded contradictory findings. A study in Wuhan, China, discovered that prenatal daily exposure to PM10 during mid-gestation was linked to higher cord blood mtDNA copy number [ 18 ], whereas another study found a significant association between higher PM2.5 exposure and lower levels of mtDNA copy number in cord blood [ 19 ]. The current body of evidence does not definitively establish the relationship between clinical outcomes and changes in mtDNA copy number induced by exposure to environmental pollutants. Therefore, further research is needed to explore the causal link between environmental contamination and mtDNA copy number. Mendelian randomization (MR) studies are an epidemiological method that addresses the limitations of observational studies and aims to establish causality. By leveraging Genome-wide Association Study (GWAS) data, genetic mutations are used as instrumental variables (IVs) with natural random assignment to evaluate the impact of a factor on a specific outcome. Analyzing genetic variants with random assignment helps reduce the influence of confounding factors, resulting in more precise causal inferences[ 20 ]. Previous MR studies have explored causal links between mtDNA copy number and prognostic risk for cardiovascular disease, cancer, and neurological disorders[ 21 – 23 ]. In this study, two-sample Mendelian randomization was employed to investigate the causal relationship between environmental pollution and mtDNA copy number while controlling for confounding variables. Methods Mendelian randomized design The basic principle of MR is to use single nucleotide polymorphism (SNP) associated with exposure and outcome as instrumental variables (IVs) to infer whether there is a causal association between the two. The exposure factor in this study was environmental pollution and the ending factor was mtDNA copy number. The basic steps included obtaining GWAS pooled data for the exposure factor and the outcome factor, screening SNPs that met the core assumptions as IVs, and performing MR analysis and sensitivity analysis. The accuracy of MR analysis was based on the fulfillment of the following three core assumptions [ 24 ]:(1) IVs selected based on the GWAS data need to be closely associated with environmental pollution; (2) IVs are not associated with confounding factors; (3) IVs affect mtDNA copy number only through environmental contamination but not through other pathways. Figure 1 shows the principle of Mendelian randomization and the flow chart of this study. Data sources Environmental pollution data sources The GWAS data selected for the study on environmental pollution encompassed two main categories: air pollution and poor working conditions. GWAS data for air pollution (including PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) were obtained from the UK Biobank, a large prospective study with more than 500,000 UK participants, for which phenotypic, genetic detail and genome-wide genotyping data have been published [ 25 – 26 ]. We used a pooled GWAS database of air pollution in European and East Asian populations. GWAS data for harsh work environments were extracted from the second round of GWAS results from the UK Biobank released in August 2018 ( http://www.nealelab.is/uk-biobank ). Harsh work environments include working in a very noisy workplace, a very cold workplace, a very hot workplace, a very dusty workplace, a workplace filled with chemicals or other fumes, a workplace with a lot of secondhand smoke, a workplace with a lot of diesel exhaust。The GWAS data for harsh work environments is only for European populations(Table 1 ). Table 1 Overview of data sources in this two-sample MR study. Exposures/Outcomes Dataset Sample Size Number of SNPs Population Consortium Sex Year Exposures Particulate matter (PM) PM2.5 ukb-b-10,817 423,796 9,851,867 European MRC-IEU Males and Females 2018 PM2.5-10 ukb-b-12,963 423,796 9,851,867 European MRC-IEU Males and Females 2018 PM10 ukb-b-589 455,314 9,851,867 European MRC-IEU Males and Females 2018 Nitrogen dioxide ukb-b-2,618 456,380 9,851,867 European MRC-IEU Males and Females 2018 Nitrogen oxides ukb-b-12,417 456,380 9,851,867 European MRC-IEU Males and Females 2018 workplace very noisy: Often ukb-d-22606_2 90653 13,566,931 European NA Males and Females 2018 workplace very cold ukb-d-22607_2 90188 12,849,021 European NA Males and Females 2018 workplace very hot ukb-d-22608_2 90165 13,296,638 European NA Males and Females 2018 workplace very dusty ukb-d-22609_2 89631 13,188,866 European NA Males and Females 2018 workplace full of chemical or other fumes ukb-d-22610_2 88735 12,324,484 European NA Males and Females 2018 workplace had a lot of cigarette smoke from other people smoking ukb-d-22611_2 89803 13,566,864 European NA Males and Females 2018 workplace had a lot of diesel exhaust ukb-d-22615_2 89104 11,409,239 European NA Males and Females 2018 Particulate matter (PM) PM2.5 um ukb-e-24006_EAS 2,505 8,268,350 Asian NA Males and Females 2020 PM2.5-10 um ukb-e-24008_EAS 2,505 8,268,350 Asian NA Males and Females 2020 PM10 um ukb-e-24005_EAS 2,505 8,268,350 Asian NA Males and Females 2020 Nitrogen dioxide ukb-e-24016_EAS 2,625 8,260,777 Asian NA Males and Females 2020 Nitrogen oxides ukb-e-24004_EAS 2,625 8,260,777 Asian NA Males and Females 2020 Outcome Mitochondrial DNA copy number ebi-a-GCST90026372 383,476 11,173,383 European NA Males and Females 2,022 Mitochondrial DNA copy number ebi-a-GCST90026373 6,172 11,181,342 Asian NA Males and Females 2,022 Table 2 Sensitivity analysis between exposure and outcome factors Exposure Horizontal pleiotropy test Horizontal pleiotropy test MR Egger intercept MR-PRESSO Global Test MR Egger Inverse variance weighted Q Q_df Q_pval Q Q_df Q_pval egger_intercept se pval pval Outliers from MR-PRESSO European PM2.5 63.498 56.000 0.229 63.582 57.000 0.256 0.000 0.001 0.786 0.235 NA PM2.5-10 25.753 22.000 0.262 27.334 23.000 0.242 -0.002 0.002 0.258 0.253 NA PM10 302.644 240.000 0.004 302.748 241.000 0.004 0.000 0.001 0.775 0.005 rs62118471,rs6447363 Nitrogen dioxide 105.177 102.000 0.395 105.510 103.000 0.413 0.000 0.001 0.571 0.434 NA Nitrogen oxides 81.888 73.000 0.223 84.814 74.000 0.183 -0.002 0.001 0.111 0.172 NA workplace full of chemical or other fumes Often 25.463 19.000 0.146 25.688 20.000 0.176 -0.001 0.002 0.687 0.162 NA workplace had a lot of cigarette smoke from other people smoking Often 17.827 16.000 0.334 18.538 17.000 0.356 0.002 0.002 0.436 0.380 NA workplace had a lot of diesel exhaust Often 36.468 20.000 0.014 37.575 21.000 0.014 0.002 0.002 0.445 0.013 NA workplace very cold 18.778 18.000 0.406 19.578 19.000 0.420 0.001 0.002 0.393 0.434 NA workplace very dusty 20.194 14.000 0.124 22.655 15.000 0.092 0.003 0.002 0.213 0.102 NA workplace very hot 14.148 12.000 0.291 17.321 13.000 0.185 0.003 0.002 0.127 0.184 NA workplace very noisy 21.285 16.000 0.168 22.361 17.000 0.171 -0.002 0.002 0.382 0.180 NA Asian NA PM2.5 4.607 3.000 0.203 4.702 4.000 0.319 -0.009 0.038 0.820 0.395 NA PM2.5-10 0.025 2.000 0.987 2.451 3.000 0.484 -0.033 0.021 0.260 0.542 NA PM10 3.558 6.000 0.736 3.558 7.000 0.829 0.000 0.019 0.983 0.838 NA Nitrogen dioxide 5.411 6.000 0.492 8.824 7.000 0.266 0.029 0.016 0.114 0.319 NA Nitrogen oxides 0.787 3.000 0.853 1.838 4.000 0.766 -0.042 0.041 0.381 0.767 NA Data sources for mitochondrial DNA copy number Mitochondrial DNA copy number was extracted from a study by Chong et al [ 27 ], involving 395,781 participants. The study identified common and rare genetic factors associated with mtDNA copy number using a novel method called Automatic Mitochondrial Replication (AutoMitoC). This method comprises four main steps: preprocessing, background correction, probe hybridization assay, and final derivation of mtDNA copy number estimates. The GWAS analysis was adjusted for gender, age, genetic principal components, and blood cell counts. The pooled GWAS data included information on mtDNA copy number in European and Asian populations(Table 1 ). Selection of instrumental variables (IVs) Based on previous studies, we were going to choose SNPs that were significantly associated with metabolites (p < 5 × 10 − 6) of SNPs [ 28 ].To obtain independent IVs, we performed clumping (R2 < 0.001 within a 1,0000-kb distance) based on the linkage disequilibrium (LD) reference panel of the 1000 Genomes Project [ 29 ]. Exposure and outcome factors of the screen were combined to ensure consistency of effect alleles. Palindromic SNPs with intermediate effect allele frequencies or SNPs with incompatible alleles were discarded [ 30 ]. To exclude the effect of weak instrumental variables on the outcome and to assess whether the included SNPs were affected by weak instrumental variables, the F statistic was used to exclude weak instrumental variables (calculated as F = β^2 / SE^2, with β being the allele effect value and SE being the standard error). If the F-statistic of SNPs is < 10, it indicates that the SNPs have the possibility of weak instrumental variable bias, and thus they are excluded to avoid the impact on the results [ 31 – 32 ]. We strictly screened the instrumental variables according to the above step sieve. Statistical methods The study utilized a two-sample Mendelian randomization approach, with environmental pollution considered as the exposure factor and mtDNA copy number as the outcome factor. Suitable instrumental variables were identified following the outlined methodology. Various methods including Inverse-Variance Weighted (IVW), weighted median (WM), simple median (SM), weighted median estimator (WME), and MR-Egger regression were employed to assess the causal relationship. In cases where only one SNP was identified in both exposure and outcome databases, analysis was conducted using the Wald ratio method. When multiple SNPs were present, the IVW method was used. If three or more SNPs were detected, all five methods mentioned above were applied[ 33 – 36 ]. In instances without heterogeneity or pleiotropy, results from the IVW method took precedence. Owing to the absence of GWAS data on adverse work environments in East Asian populations, the study focused solely on analyzing the causal effects of air pollution components (such as PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) on mtDNA copy number within East Asian populations. The findings were presented in terms of odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs). Sensitivity analysis Instrumental variables from various sources, experiments, and populations may exhibit heterogeneity, potentially impacting Mendelian randomization analysis outcomes. To assess this heterogeneity, Cochran's Q statistic (IVW) and Rucker's Q statistic (MR-Egger) were utilized, with a significance level of p < 0.05 indicating heterogeneity within the study. [ 37 ]. When instrumental variables influence the occurrence of an outcome through factors other than the exposure factor, it indicates that the instrumental variables are polyvalent. pleiotropy can lead to failure of the independence and exclusivity assumptions. With MR-Egger intercept, it is possible to detect the pleiotropy of the data and to assess the robustness of the results. If p < 0.05, it indicates the presence of pleiotropy in the data [ 38 ].MR-PRESSO uses the global test to detect horizontal pleiotropy and, if necessary, to correct potential pleiotropy outliers through outlier removal.The MR Steiger test examines causality direction, while Leave-one-out analyses assess the impact of individual SNP removal on results. If the removal of a SNP does not significantly alter the outcomes, it suggests that the SNP does not have a nonspecific effect on the results. [ 39 ]. Statistical analysis All data analyses were performed using the R language (version 4.3.0) software packages "Two-Sample-MR" "MR-PRESSO" and "mr.raps" programme packages were carried out[ 40 ]. Results Selection of instrumental variables In the European population, we screened 58 SNPs (PM2.5), 24 SNPs (PM2.5-10), 242 SNPs (PM10), 104 SNPs (nitrogen dioxide), 75 SNPs (nitrogen oxides), 21 SNPs ( workplace full of chemical or other fumes), 18 SNPs (workplace had a lot of cigarette smoke from other people smoking), 22 SNPs (workplace had a lot of diesel exhaust ), 20 SNPs (workplace very cold ), 16 SNPs (workplace very dusty ), 14 SNPs (workplace very hot ), and 18 SNPs (workplace very noisy ). In the Asian population, we screened 5 SNPs (PM2.5), 4 SNPs (PM2.5-10), 8 SNPs (PM10), 8 SNPs (nitrogen dioxide), and 5 SNPs (nitrogen oxides) from 5 exposure factors. Data for all SNPs closely related to exposure factors are in Supplementary Table 1. After calculation, the F-statistic values of all SNPs were greater than 10, indicating that the IVs had a small bias and could be analyzed by MR. Causal relationship between environmental pollution and mtDNA copy number in European populations There was no causal relationship between the 12 exposures and mtDNA copy number in the European population. The following advantage ratios (OR) and 95% confidence intervals (CI) for various exposures were shown in our MR study: PM2.5 (OR = 0.973, 95% CI 0.921–1.029, p = 0.341), PM2.5-10 (OR = 0.997, 95% CI 0.912–1.091, p = 0.954), PM10 (OR = 0.994, 95% CI 0.964–1.025, p = 0.710), nitrogen dioxide (OR = 0.973, 95% CI 0.932–1.014, p = 0.196), nitrogen oxides (OR = 0.983, 95% CI 0.934–1.035, p = 0.524), workplace full of chemical or other fumes Often (OR = 0.130, 95% CI 0.930–1.136, p = 0.194), workplace had a lot of cigarette smoke from other people smoking Often (OR = 1.012, 95% CI 0.897–1.142, p = 0.847), workplace had a lot of diesel exhaust Often (OR = 1.058, 95% CI 0.812–1.379, p = 0.677), workplace very cold Often (OR = 1.047, 95% CI 0.903–1.215, p = 0.541), workplace very cold Often (OR = 1.028, 95% CI 0.847–1.248, p = 0.778), workplace very hot Often ( OR = 0.948, 95% CI 0.793–1.132, p = 0.554), and workplace very noisy Often (OR = 0.998, 95% CI 0.880–1.131, p = 0.973). The results of all MR analyses are shown in Figs. 2 – 3 . Causal relationship between environmental pollution and mtDNA copy number in East Asian populations We explored the causal relationship between only five exposure factors (PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) and mtDNA copy number. The results showed no causal relationship between any of the five exposure factors and mtDNA copy number. The following are the odds ratios (OR) and 95% confidence intervals (CI) for the various exposures shown in our MR study: PM2.5 (OR = 1.001, 95% CI 0.877–1.142, p = 0.990), PM2.5-10 (OR = 0.982, 95% CI 0.887–1.089, p = 0.739), PM10 (OR = 1.025, 95% CI 0.948–1.107, p = 0.537), nitrogen dioxide (OR = 1.039, 95% CI 0.960–1.125, p = 0.341), and nitrogen oxides (OR = 1.018, 95% CI 0.917–1.131, p = 0.735). The results of all MR analyses are shown in Figs. 4 – 5 . sensitivity analysis Heterogeneity and multiplicity analyses were performed in both the European and Asian populations to verify the stability of the causal relationships we obtained through MR analysis. Heterogeneity analysis: no significant heterogeneity was observed in both the IVW test and the MR-Egger test. Analysis of pleiotropy: the MR-Egger intercept test showed a p-value > 0.05, indicating the absence of horizontal pleiotropy. PM10 (p = 0.005) and workplace had a lot of diesel exhaust Often (p = 0.013) with mtDNA copy number in the European population were statistically significant in MR-PRESSO. There were outliers rs62118471, and rs6447363 between PM10 and mtDNA copy numbers in the European population. after the removal of the outliers, there was still no statistical significance in the MR analysis of PM10 versus mtDNA copy number (Supplementary Material). Heterogeneity and pleiotropy analyses indicated the stability of the results (Table 2). After removing SNPs one by one, respectively, by the leave-one-out method, effect size estimation was performed for the remaining SNPs, and the results showed that there was no large difference between the effect sizes before and after removal, suggesting that no single SNP had a significant effect on the MR estimation results (Figs. 6 – 7 ). Discussion This study utilized GWAS data on environmental contamination and mtDNA copy number from public databases. It is the first Mendelian randomization study to investigate the potential causal relationship between environmental contamination and mtDNA copy number. Despite thorough quality control measures, no definitive evidence supporting a direct causal link between environmental contamination and mtDNA copy number was found. Further research is needed to explore the impact of environmental pollution on changes in mtDNA copy number, as these changes may be influenced by a combination of environmental factors and other variables rather than directly caused by environmental pollution. Results from a cross-sectional study conducted among non-smoking women in rural China revealed a significant negative association between short-term exposure to indoor PM2.5 concentrations and peripheral blood mtDNA copy number levels [ 41 ]. Similarly, a multicenter study in China found that individuals exposed to higher mean PM2.5 concentrations in Zhuhai had lower peripheral blood mtDNA copy numbers [ 42 ]. However, inconsistent results were reported in a repeated-measures study involving patients with type 2 diabetes, showing no short-term association between PM or gaseous pollutants and blood mtDNA copy number [ 43 ]. These discrepancies in findings could be attributed to variations in the susceptibility of study populations and differences in the sources and components of PM2.5. An epidemiologic study by Pieters et al. indicated that brief exposures to PM2.5 were linked to increased mtDNA copy numbers, while sustained exposures were associated with decreased mtDNA copy numbers [ 44 ]. This phenomenon may be explained by the fact that short-term exposure to particulate matter triggers an acute inflammatory response, leading to a temporary rise in mtDNA copy number. It is suggested that this increase in mtDNA copy number might serve as an adaptive mechanism during the initial phase of exposure to harmful pollutants. However, as exposure duration increases, oxidative stress induced by particulate matter becomes dominant, resulting in damage to mitochondrial DNA and subsequent reduction in mtDNA copy number. [ 45 – 46 ]. An increase in mtDNA copy number due to short-term exposure to airborne particulate matter has been similarly reported in an occupational population of healthy male steelworkers [ 47 ]. This suggests that mtDNA copy number could potentially serve as a mediator in the relationship between air pollution and the development of chronic diseases. Previous research has shown that prenatal exposure to heavy metals such as Cd, Pb, and As can impact neonatal mtDNA copy number [ 48 – 51 ], although findings have not been entirely consistent. For instance, while Sanchez-Guerra et al. observed a rise in neonatal cord blood mtDNA copy number associated with Pb exposure in mid- and late pregnancy [ 48 ], Smith et al. did not find a significant link between Pb exposure in early pregnancy and cord blood mtDNA copy number [ 49 ]. The varying conclusions of these studies may be attributed to differences in the timing of exposure assessments. Therefore, further investigation is warranted to explore the relationship between heavy metal exposure and neonatal mtDNA copy number by collecting samples at multiple time points during pregnancy and identifying the potential window of sensitivity to prenatal heavy metal exposure on neonatal mtDNA copy number. Environmental pollution causes oxidative stress in the organism and the copy number of mtDNA in the cell changes in response to the excessive reactive oxygen species (ROS) produced by oxidative stress in the cell [ 50 ]. Mitochondria are the main source of ROS, and the accumulation of ROS can easily cause oxidative damage to mitochondrial DNA. mtDNA copy number changes can reflect the degree of oxidative stress and mitochondrial damage [ 51 ]. mtDNA copy number increase is an effective mechanism to cope with oxidative stress, and can effectively buffer the damage caused by oxidative stress [ 52 ]. When the organism undergoes elevated levels of oxidative stress, the mitochondrial repair mechanism is insufficient to remove damaged mitochondrial DNA, and at the same time the mitochondrial function is unable to meet the needs of the organism, resulting in the activation of the pathway of p53 and peroxisome proliferator-activated receptor γ coactivator-1α [ 53 – 54 ], which exhibits the elevation of the mtDNA copy number to satisfy the needs of normal cellular function. Long-term stress response of the organism leads to increased accumulation of ROS, and mitochondrial dysfunction reduces the mitochondrial content, which is manifested as a decrease in mtDNA copy number [ 55 ]. Our study has several strengths. We explored the causal relationship between environmental contamination and mtDNA copy number using a two-sample Mendelian randomization study approach. This method allows for causality analysis of exposure and outcome by utilizing public GWAS data, making it more resource-efficient compared to the randomized controlled trial approach. Previous studies were limited in controlling for confounders due to the inability to conduct randomized controlled trials. Observational studies have yielded inconclusive results possibly influenced by confounding bias or causality reversal. By employing a GWAS-based MR method, we effectively mitigated confounding factors and causality reversal to analyze the causal association between environmental pollution and mtDNA copy number at the gene level. However, it is important to acknowledge certain limitations of this study. Firstly, the analysis focused solely on the causal relationship between environmental pollution and mtDNA copy number, without exploring the potential impact of other factors. The regulation of mtDNA copy number is a multifaceted process that may be influenced by various variables, which should be considered in future research. Secondly, the GWAS data utilized in this study predominantly originated from European populations, with limited representation from Asian or other populations, potentially restricting the generalizability of the findings. Thirdly, while the MR method employed is a robust approach for causal analysis, it is essential to conduct animal experiments in the future to validate any potential causal link between environmental contamination and mtDNA copy number. Lastly, although the results of the heterogeneity and multicollinearity analyses we performed showed no obvious abnormalities, bias due to multicollinearity or exponential event bias is inevitable. In conclusion, our study did not identify a direct causal relationship between environmental pollution and mtDNA copy number. Nonetheless, the observed relationship warrants further investigation to fully understand its implications. Declarations Data availability statement The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author. Author Contributions Conceptualization and design: BBZ, KWL; Data collection and integration: CL, ZXDW, SQW, and XL; Data analysis and interpretation: CL,QY, ZXDW, SQW, XL,WZG; Writing of the first draft of the manuscript:BBZ; Manuscript review and editing: BBZ,DB,CL,QY, ZXDW, SQW, XL,WZG,KWL. All authors reviewed and agreed to the final version of the manuscript. Acknowledgments We thank the IEU Open GWAS 340 project (https://gwas.mrcieu.ac.uk/datasets/) for providing summary results data for the analyses. Competing interests The authors declare no competing interests. References Marais EA, Vohra K, Kelly JM, et al. The health burden of air pollution in the UK: a modeling study using updated exposure-risk associations. lancet. 2023;402 Suppl 1:S66 Cohen AJ, Brauer M, Burnett R, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. lancet. 2017;389 (10082):1907–1918. Sin DD, Doiron D, Agusti A, et al. Air pollution and COPD: GOLD 2023 committee report. Eur Respir J. 2023;61 (5):. Eur Respir J. 2023;61 (5):. Friedrich MJ. Air Pollution Is Greatest Environmental Threat to Health. JAMA. 2018;319 (11):1085. Apte JS, Marshall JD, Cohen AJ, et al. Addressing Global Mortality from Ambient PM2.5. Environ Sci Technol. 2015;49 (13):8057–66. Cohen AJ, Brauer M, Burnett R, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. lancet. 2017;389 (10082):1907–1918. Ledda C, Iavicoli I, Bracci M, et al. Serum lipid, lipoprotein, and apolipoprotein profiles in workers exposed to low arsenic levels: lipid profiles and occupational arsenic exposure. Toxicol Lett. 2018;282:49–56. Demers PA, DeMarini DM, Fent KW, et al. Carcinogenicity of occupational exposure as a firefighter. Lancet Oncol. 2022;23 (8):985–986. Zhang C, Wang J, Wang H, et al. Surveillance of noise exposure level in industrial enterprises- Jiangsu Province, China, 2022. Front Public Health. 2024;12:1230481. Clyde D. Mitochondrial DNA copy number and disease. Nat Rev Genet. 2022;23 (3):136. Memon AA, Vats S, Sundquist J, et al. Mitochondrial DNA Copy Number: Linking Diabetes and Cancer. Antioxid Redox Signal. 2022;37 (16–18):1168–1190. Hong YS, Longchamps RJ, Zhao D, et al. Mitochondrial DNA Copy Number and Incident Heart Failure: The Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2020;141 (22):1823–1825. Yuan Y, Ju YS, Kim Y, et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nat Genet. 2020;52 (3):342–352. Nat Genet. 2020;52 (3):342–352. Qiao JC, Sun LJ, Zhang MY, et al. Association between ambient particulate matter exposure and mitochondrial DNA copy number: A systematic review and meta-analysis. Sci Total Environ. 2024;923:171423. Meyer JN, Hartman JH, Mello DF. Mitochondrial Toxicity. Toxicol Sci. 2018;162 (1):15–23. Wang X, Hart JE, Liu Q, et al. Association of particulate matter air pollution with leukocyte mitochondrial DNA copy number. Environ Int. 2020;141. 105761. Li R, Li S, Pan M, et al. Physical activity counteracted associations of exposure to mixture of air pollutants with mitochondrial DNA copy number among rural Chinese adults. Chemosphere. 2021;272:129907. Hu C, Sheng X, Li Y, et al. Effects of prenatal exposure to particulate air pollution on newborn mitochondrial DNA copy number. chemosphere. 2020;253:126592. Rosa MJ, Just AC, Guerra MS, et al. Identifying sensitive windows for prenatal particulate air pollution exposure and mitochondrial DNA content in cord blood. environ Int. 2017;98:198–203. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data.Genet Epidemiol. 2013;37 (7):658–65. Cai X, Liang C, Zhang M, et al. Mitochondrial DNA copy number and cancer risks: a comprehensive Mendelian randomization analysis. Int J Cancer. 2024;. 154 (8):1504–1513. Qin P, Qin T, Liang L, et al. The role of mitochondrial DNA copy number in cardiometabolic disease: a bidirectional two-sample mendelian randomization study. Cardiovasc Diabetol. 2024;23 (1):45. Chong MR, Narula S, Morton R, et al. Mitochondrial DNA Copy Number as a Marker and Mediator of Stroke Prognosis: Observational and Mendelian Randomization Analyses. Neurology. 2021;:. Yu TQ, Xu WT, Su YN, et al.Basic principles, methods, and limitations of Mendelian randomization research[J]. Journal of Evidence-Based Medicine, 2021,21(10):1227–1234. Eeftens M, Beelen R, de Hoogh K, et al. Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) in 20 European study areas; results of the ESCAPE project. Environ Sci Technol. 2012;46 (20):11195–205. Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. nature. 2018;562 (7726):203–209. Chong M, Mohammadi-Shemirani P, Perrot N, et al. GWAS and ExWAS of blood mitochondrial DNA copy number identify 71 loci and highlights a potential causal role in dementia. Elife. 2022;11:. Elife. 2022;11: Xue Y, Zhang L, Chen Y, et al. Gut microbiota and atopic dermatitis: a two-sample Mendelian randomization study. Front Med (Lausanne). 2023;10:1174331. Cai J, Li X, Wu S, Tian Y, Zhang Y, Wei Z, et al. Assessing the causal association between human blood metabolites and the risk of epilepsy. J Transl Med (2022) 20(1):437. Hartwig FP, Davies NM, Hemani G, et al. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol. 2016;45 (6):1717–1726. Burgess S, Thompson SG, Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40 (3):755–64. doi: 10.1093/ije/dyr036 Feng R, Lu M, Xu J, et al. Pulmonary embolism and 529 human blood metabolites: genetic correlation and two-sample Mendelian randomization study. BMC Genom Data. 2022;23 (1):69. TTachmazidou I, Hatzikotoulas K, Southam L, et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet. 2019;51 (2):230–236. Burgess S, Bowden J, Fall T, et al. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. epidemiology. 2017;28 (1):30–42. Slob EA, Groenen PJ, Thurik AR, et al. A note on the use of Egger regression in Mendelian randomization studies [JJ. Int J Epidemiol, 2017, 46(6) -2094- 7. Slob EAW, Groenen PJF, Thurik AR, et al. A note on the use of Egger regression in Mendelian randomization studies. Int J Epidemiol. 2017;46 (6):2094–2097. Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50 (5):693–698. Greco M FD, Minelli C, Sheehan NA, et al. Detecting pleiotropy in Mendelian randomization studies with summary data and a continuous outcome. stat. Med. 2015;34 (21):2926–40. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44 (2):512–25. Hemani G,Zheng J,Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7: Wong JYY, Hu W, Downward GS, et al. Personal exposure to fine particulate matter and benzo[a]pyrene from indoor air pollution and leukocyte mitochondrial DNA copy number in rural China. Carcinogenesis. 2017;38 (9):893–899. Li Z, Zhu M, Du J, et al. Genetic variants in nuclear DNA along with environmental factors modify mitochondrial DNA copy number: a population-based exome-wide association study. BMC Genomics. 2018;19 (1):752. Xia Y,Chen R,Wang C, et al. Ambient air pollution, blood mitochondrial DNA copy number, and telomere length in a panel of diabetes patients. Inhal Toxicol. 2015;27 (10):481–7. Pieters N, Janssen BG, Dewitte H, et al. Biomolecular Markers within the Core Axis of Aging and Particulate Air Pollution Exposure in the Elderly: A Cross-Sectional Study. Environ Health Perspect. 2016;124 (7):943–50. Gustafsson CM,Falkenberg M,Larsson NG. Maintenance and Expression of Mammalian Mitochondrial DNA. Annu Rev Biochem. 2016;85:133–60. Byun HM, Baccarelli AA. Environmental exposure and mitochondrial epigenetics: study design and analytical challenges. Hum Genet. 2014;133 (3). 247–57. Hou L, Zhu ZZ, Zhang X, et al. Airborne particulate matter and mitochondrial damage: a cross-sectional study. Environ Health. 2010;9:48. Vriens A, Nawrot TS, Baeyens W, et al. Neonatal exposure to environmental pollutants and placental mitochondrial DNA content: a multi-pollutant approach. environ Int. 2017;106:60–68. Song L, Liu B, Wang L, et al. Exposure to arsenic during pregnancy and newborn mitochondrial DNA copy number: a birth cohort study in Wuhan, China. Chemosphere. 2020;243:125335. Liu J, Jia DY, Cai SZ, et al. Mitochondria defects are involved in lead-acetate-induced adult hematopoietic stem cell decline. Toxicol Lett. 2015;. 235 (1):37–44. Malik AN, Czajka A. Is mitochondrial DNA content a potential biomarker of mitochondrial dysfunction? Mitochondrion. 2013;13 (5):481–92. Meyer JN, Leung MC, Rooney JP, et al. Mitochondria as a target of environmental toxicants. Toxicol Sci. 2013;134 (1):1–17. doi: 10.1093/toxsci/ kft102 Wen S, Gao J, Zhang L, et al. p53 increase mitochondrial copy number via up-regulation of mitochondrial transcription factor A in colorectal cancer. Oncotarget. 2016;7 (46):75981–75995. Dabrowska A,Venero JL, Iwasawa R, et al. PGC-1α controls mitochondrial biogenesis and dynamics in lead-induced neurotoxicity. Aging (Albany NY). 2015;7 (9):629–47. Clay Montier LL, Deng JJ, Bai Y. Number matters control of mammalian mitochondrial DNA copy number. J Genet Genomics. 2009;36 (3):125–31. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4506104","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310420882,"identity":"1c689652-34cc-4e05-85a5-873d9bd8d2f2","order_by":0,"name":"Binbin Zhang","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Zhang","suffix":""},{"id":310420883,"identity":"db8e1cbf-d207-4bf5-b8e3-9667fbd1f807","order_by":1,"name":"Bin Dou","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Dou","suffix":""},{"id":310420884,"identity":"879befdf-11c6-4496-bdd6-c72439a44b45","order_by":2,"name":"Chuan Lu","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Lu","suffix":""},{"id":310420885,"identity":"537ed9ed-e74d-4cc3-afc9-8d957cf47dc4","order_by":3,"name":"Qi Yan","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Yan","suffix":""},{"id":310420886,"identity":"52021aa8-a433-4dfe-9844-da1196bb2b84","order_by":4,"name":"Dawa Zhaxi","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dawa","middleName":"","lastName":"Zhaxi","suffix":""},{"id":310420887,"identity":"63d08954-ea6f-4a38-a16d-fd65355c9acb","order_by":5,"name":"Shuqing Wei","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuqing","middleName":"","lastName":"Wei","suffix":""},{"id":310420888,"identity":"1a0f6313-382e-4b69-8355-483c19aacb47","order_by":6,"name":"Xiang Luo","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Luo","suffix":""},{"id":310420889,"identity":"21c1f986-7e6c-433d-a566-42b8f4fb8f51","order_by":7,"name":"Wenzuo Gu","email":"","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenzuo","middleName":"","lastName":"Gu","suffix":""},{"id":310420890,"identity":"e97a933c-6260-4619-a827-ff7f9c7dcda8","order_by":8,"name":"Kewen Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYFCCxAeMDQwWcmzszQeI1ZJsANQiYczHcyyBNC2J8yRyFIjTwHc8mU1yZptEehtDDgPDj4pthLVInnnMJrmxTSK3jeHsAcaeM7cJazG4kX9M8uE2oBbGvgRmxjaitAAdBtSSzsbMY0CClo3bJBLY2IjVAvQLs+XMfxKGbTxsCQeJ8gswxBhv9pyxkZef//jggx8VRGhhOMDAIoFgEwUOMDB/IE7lKBgFo2AUjFgAAGelPOuh9j9oAAAAAElFTkSuQmCC","orcid":"","institution":"Qinghai University Affiliated Hospital Xining","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kewen","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-05-31 04:15:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4506104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4506104/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58271886,"identity":"746227af-9e58-4e6e-b3f0-f82d242b438a","added_by":"auto","created_at":"2024-06-13 08:50:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":402985,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the bidirectional Mendelian randomization framework study design for studying the causal effect of environmental pollution on mitochondrial DNA copy number. In total, we performed 17 magnetic resonance analyses to investigate the association between environmental pollution and mitochondrial DNA copy number in Asian and European populations, respectively. All genetic instruments are single nucleotide polymorphisms (SNPs).\u003c/p\u003e","description":"","filename":"Figure1Theoverviewflowchartofhypothesisandschematicdesign.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/c844beca9db67b8f4863ba02.jpg"},{"id":58272701,"identity":"4f01bfa8-3928-4573-92fc-ddf82ff9a950","added_by":"auto","created_at":"2024-06-13 08:58:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1788279,"visible":true,"origin":"","legend":"\u003cp\u003eResults on the association between environmental pollution and mitochondrial DNA copy number levels in European population were analysed using five Mendelian randomization (MR) methods.OR:odds radio; CI:confidence interval; IVW:inverse variance weighted; SNPs:single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"Figure2ResultsofMRAnalysisinaEuropeanpopulation00.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/a245c5cc5e07ff090c7350dd.jpg"},{"id":58271893,"identity":"0923d511-5ea2-4f4e-8f19-86d9def7242e","added_by":"auto","created_at":"2024-06-13 08:50:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":509025,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the association between environmental pollution and mitochondrial DNA copy number in European population.Each black point representing each SNP on the exposure (horizontal-axis) and on the outcome (vertical-axis) is plotted with error bars corresponding to each standard error.The slope of each line corresponds to the combined estimate using each method of the inverse variance weighted (light blue line), the MR-Egger (blue line), the simple mode (light green line), the weighted median (green line), and the weighted mode (pink line).IVW:inverse variance weighting,SNPs:single nucleotide polymorphisms,Outcome:mitochondrial DNA copy number.A:PM2.5,B:PM2.5-10,C:PM10,D:Nitrogen dioxide,E:Nitrogen oxides,F:workplace full of chemical or other fumes ,G:workplace had a lot of cigarette smoke from other people smoking ,H:workplace had a lot of diesel exhaust,I:workplace very cold,J:workplace very dusty,K:workplace very hot,\u003c/p\u003e\n\u003cp\u003eL:workplace very noisy.\u003c/p\u003e","description":"","filename":"Figure3ScatterplotofcausalSNPeffectsintheEuropeanpopulation.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/3292ef136e3a5089f061622d.jpg"},{"id":58273450,"identity":"9c0c5993-735c-43d5-a8a4-2b38ac75f2f6","added_by":"auto","created_at":"2024-06-13 09:06:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1011675,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of Leave-one-out analyses of the association between environmental pollution and mitochondrial DNA copy number in European population.The error bars indicate the 95% confidence interval (CI).A:PM2.5,B:PM2.5-10,C:PM10,D:Nitrogen dioxide,E:Nitrogen oxides,F:workplace full of chemical or other fumes ,G:workplace had a lot of cigarette smoke from other people smoking ,H:workplace had a lot of diesel exhaust,I:workplace very cold,J:workplace very dusty,K:workplace very hot,\u003c/p\u003e\n\u003cp\u003eL:workplace very noisy.\u003c/p\u003e","description":"","filename":"Figure4ForestplotsofLeaveoneoutanalysesforcausalSNPeffectinEuropeanpopulation..jpg","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/b445bddb9485ad9c14b4388b.jpg"},{"id":58272703,"identity":"7d9ff5c1-0b86-471d-adc0-6f32cdc09cb3","added_by":"auto","created_at":"2024-06-13 08:58:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":642998,"visible":true,"origin":"","legend":"\u003cp\u003eResults on the association between environmental pollution and mitochondrial DNA copy number levels in Asian populations were analyzed using five Mendelian randomization (MR) methods.OR:odds radio,CI:confidence interval,IVW:inverse variance weighted, SNPs:single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"Figure5ResultsofMRAnalysisinanAsianpopulation00.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/e7cf7389670ac525c30f1350.jpg"},{"id":58271890,"identity":"d29886fe-874a-4212-b682-bc19e80340d7","added_by":"auto","created_at":"2024-06-13 08:50:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":142858,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the association between environmental pollution and mitochondrial DNA copy number in Asian population.Each black point representing each SNP on the exposure (horizontal-axis) and on the outcome (vertical-axis) is plotted with error bars corresponding to each standard error.The slope of each line corresponds to the combined estimate using each method of the inverse variance weighted (light blue line), the MR-Egger (blue line), the simple mode (light green line), the weighted median (green line), and the weighted mode (pink line).IVW:inverse variance weighting,SNPs:single nucleotide polymorphisms,Outcome:mitochondrial DNA copy number.A:PM2.5,B:PM2.5-10,C:PM10,D:Nitrogen dioxide,E:Nitrogen oxides.\u003c/p\u003e","description":"","filename":"Figure6ScatterplotofcausalSNPeffectsintheAsianpopulation.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/b5cbd6494100a61c68d276ef.jpg"},{"id":58273451,"identity":"6308a1aa-585b-4ea4-b4e9-155d88875f09","added_by":"auto","created_at":"2024-06-13 09:06:45","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":127609,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of Leave-one-out analyses of the association between environmental pollution and mitochondrial DNA copy number in Asian population.The error bars indicate the 95% confidence interval (CI).A:PM2.5,B:PM2.5-10,C:PM10,D:Nitrogen dioxide,E:Nitrogen oxides.\u003c/p\u003e","description":"","filename":"Figure7ForestplotsofLeaveoneoutanalysesforcausalSNPeffectinAsianpopulation..jpg","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/b5b33c92ff6c2b3f35872c98.jpg"},{"id":60658168,"identity":"c26cd7dd-49fd-4824-a2a5-664993dd7762","added_by":"auto","created_at":"2024-07-19 07:56:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5376005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/e5b18bdc-cfc6-494e-9394-7b09b8103385.pdf"},{"id":58271885,"identity":"239c9bfa-9eb3-4796-b14e-02621e717251","added_by":"auto","created_at":"2024-06-13 08:50:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":527018,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-4506104/v1/c8f3c7df0f86153acc3f02aa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between environmental pollution and mitochondrial DNA copy number in European and East Asian populations: a Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRapid economic and social development has introduced new health risks [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these risks, atmospheric pollution has emerged as a critical global public health concern [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, atmospheric particulate matter (PM) is identified as the most chemically complex and hazardous pollutant in the atmosphere [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A 25-year study revealed that air pollution significantly impacts the global burden of disease, with PM2.5 ranking as the fifth risk factor for mortality in 2015 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, exposure to diverse work environments can lead to various occupational diseases among workers [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Identifying key disease-causing environmental factors from numerous risk factors poses a significant challenge for environmental health research. Mitochondrial DNA (mtDNA) is the sole genome present in animal cell organelles, responsible for controlling and coding certain proteins, with its quantity in the genome termed as mtDNA copy number [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Variations in mtDNA copy number have been linked to metabolic syndrome, neurodegenerative diseases, cardiovascular diseases, and cancer. The high specificity of mtDNA copy number is making it a progressively utilized disease biomarker [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In contrast to the nuclear genome, mtDNA lacks an efficient damage repair system, making it more susceptible to environmental, genetic, and other influences, resulting in alterations in replication, transcription levels, and copy number [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the precise mechanisms underlying these changes remain unclear, and studying mtDNA copy number stands as a crucial pathway to furthering our understanding of mitochondria and disease progression.\u003c/p\u003e \u003cp\u003eIn recent years, there has been growing evidence linking exposure to Particulate Matter (PM) in the atmosphere with changes in mtDNA copy number [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Environmental pollutants have been shown to trigger mitochondrial toxicity, resulting in oxidative stress, disruption of mitochondrial ATP production, and interference with mitochondria-mediated cell signaling [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, existing studies have produced conflicting results. For instance, a study involving 2758 women reported that prolonged exposure to PM2.5 decreased leukocyte mtDNA copy number [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while another study suggested that long-term air pollution exposure actually increased mtDNA copy number in the body [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, research on air pollution and placental mtDNA copy number has yielded contradictory findings. A study in Wuhan, China, discovered that prenatal daily exposure to PM10 during mid-gestation was linked to higher cord blood mtDNA copy number [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], whereas another study found a significant association between higher PM2.5 exposure and lower levels of mtDNA copy number in cord blood [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The current body of evidence does not definitively establish the relationship between clinical outcomes and changes in mtDNA copy number induced by exposure to environmental pollutants. Therefore, further research is needed to explore the causal link between environmental contamination and mtDNA copy number.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) studies are an epidemiological method that addresses the limitations of observational studies and aims to establish causality. By leveraging Genome-wide Association Study (GWAS) data, genetic mutations are used as instrumental variables (IVs) with natural random assignment to evaluate the impact of a factor on a specific outcome. Analyzing genetic variants with random assignment helps reduce the influence of confounding factors, resulting in more precise causal inferences[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Previous MR studies have explored causal links between mtDNA copy number and prognostic risk for cardiovascular disease, cancer, and neurological disorders[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, two-sample Mendelian randomization was employed to investigate the causal relationship between environmental pollution and mtDNA copy number while controlling for confounding variables.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomized design\u003c/h2\u003e \u003cp\u003eThe basic principle of MR is to use single nucleotide polymorphism (SNP) associated with exposure and outcome as instrumental variables (IVs) to infer whether there is a causal association between the two. The exposure factor in this study was environmental pollution and the ending factor was mtDNA copy number. The basic steps included obtaining GWAS pooled data for the exposure factor and the outcome factor, screening SNPs that met the core assumptions as IVs, and performing MR analysis and sensitivity analysis. The accuracy of MR analysis was based on the fulfillment of the following three core assumptions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]:(1) IVs selected based on the GWAS data need to be closely associated with environmental pollution; (2) IVs are not associated with confounding factors; (3) IVs affect mtDNA copy number only through environmental contamination but not through other pathways. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the principle of Mendelian randomization and the flow chart of this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eEnvironmental pollution data sources\u003c/h2\u003e \u003cp\u003eThe GWAS data selected for the study on environmental pollution encompassed two main categories: air pollution and poor working conditions. GWAS data for air pollution (including PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) were obtained from the UK Biobank, a large prospective study with more than 500,000 UK participants, for which phenotypic, genetic detail and genome-wide genotyping data have been published [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We used a pooled GWAS database of air pollution in European and East Asian populations. GWAS data for harsh work environments were extracted from the second round of GWAS results from the UK Biobank released in August 2018 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nealelab.is/uk-biobank\u003c/span\u003e\u003cspan address=\"http://www.nealelab.is/uk-biobank\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Harsh work environments include working in a very noisy workplace, a very cold workplace, a very hot workplace, a very dusty workplace, a workplace filled with chemicals or other fumes, a workplace with a lot of secondhand smoke, a workplace with a lot of diesel exhaust。The GWAS data for harsh work environments is only for European populations(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of data sources in this two-sample MR study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExposures/Outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParticulate\u003c/p\u003e \u003cp\u003ematter (PM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-10,817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423,796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2.5-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-12,963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423,796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455,314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNitrogen dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-2,618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNitrogen oxides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-12,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eworkplace very noisy: Often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-22606_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,566,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eworkplace very cold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-22607_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,849,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eworkplace very hot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-22608_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,296,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eworkplace very dusty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-22609_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,188,866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eworkplace full of chemical or other fumes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-22610_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,324,484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eworkplace had a lot of cigarette smoke from other people smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-22611_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,566,864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eworkplace had a lot of diesel exhaust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-d-22615_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,409,239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParticulate\u003c/p\u003e \u003cp\u003ematter (PM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2.5 um\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-e-24006_EAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,268,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2.5-10 um\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-e-24008_EAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,268,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM10 um\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-e-24005_EAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,268,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNitrogen dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-e-24016_EAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,260,777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNitrogen oxides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-e-24004_EAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,260,777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMitochondrial DNA copy number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eebi-a-GCST90026372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383,476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,173,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMitochondrial DNA copy number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eebi-a-GCST90026373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,181,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;2 Sensitivity analysis between exposure and outcome factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eExposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHorizontal pleiotropy test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHorizontal pleiotropy test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c10\" namest=\"c8\" rowspan=\"2\"\u003e \u003cp\u003eMR Egger intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003cp\u003eGlobal Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ_df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ_df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eegger_intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eOutliers from MR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2.5-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e302.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e241.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ers62118471,rs6447363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen oxides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworkplace full of chemical or other fumes Often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworkplace had a lot of cigarette smoke from other people smoking Often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworkplace had a lot of diesel exhaust Often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworkplace very cold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworkplace very dusty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworkplace very hot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworkplace very noisy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2.5-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen oxides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eData sources for mitochondrial DNA copy number\u003c/h2\u003e \u003cp\u003eMitochondrial DNA copy number was extracted from a study by Chong et al [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], involving 395,781 participants. The study identified common and rare genetic factors associated with mtDNA copy number using a novel method called Automatic Mitochondrial Replication (AutoMitoC). This method comprises four main steps: preprocessing, background correction, probe hybridization assay, and final derivation of mtDNA copy number estimates. The GWAS analysis was adjusted for gender, age, genetic principal components, and blood cell counts. The pooled GWAS data included information on mtDNA copy number in European and Asian populations(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSelection of instrumental variables (IVs)\u003c/h2\u003e \u003cp\u003eBased on previous studies, we were going to choose SNPs that were significantly associated with metabolites (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;6) of SNPs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].To obtain independent IVs, we performed clumping (R2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within a 1,0000-kb distance) based on the linkage disequilibrium (LD) reference panel of the 1000 Genomes Project [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Exposure and outcome factors of the screen were combined to ensure consistency of effect alleles. Palindromic SNPs with intermediate effect allele frequencies or SNPs with incompatible alleles were discarded [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To exclude the effect of weak instrumental variables on the outcome and to assess whether the included SNPs were affected by weak instrumental variables, the F statistic was used to exclude weak instrumental variables (calculated as F\u0026thinsp;=\u0026thinsp;β^2 / SE^2, with β being the allele effect value and SE being the standard error). If the F-statistic of SNPs is \u0026lt;\u0026thinsp;10, it indicates that the SNPs have the possibility of weak instrumental variable bias, and thus they are excluded to avoid the impact on the results [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We strictly screened the instrumental variables according to the above step sieve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eThe study utilized a two-sample Mendelian randomization approach, with environmental pollution considered as the exposure factor and mtDNA copy number as the outcome factor. Suitable instrumental variables were identified following the outlined methodology. Various methods including Inverse-Variance Weighted (IVW), weighted median (WM), simple median (SM), weighted median estimator (WME), and MR-Egger regression were employed to assess the causal relationship. In cases where only one SNP was identified in both exposure and outcome databases, analysis was conducted using the Wald ratio method. When multiple SNPs were present, the IVW method was used. If three or more SNPs were detected, all five methods mentioned above were applied[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In instances without heterogeneity or pleiotropy, results from the IVW method took precedence. Owing to the absence of GWAS data on adverse work environments in East Asian populations, the study focused solely on analyzing the causal effects of air pollution components (such as PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) on mtDNA copy number within East Asian populations. The findings were presented in terms of odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eInstrumental variables from various sources, experiments, and populations may exhibit heterogeneity, potentially impacting Mendelian randomization analysis outcomes. To assess this heterogeneity, Cochran's Q statistic (IVW) and Rucker's Q statistic (MR-Egger) were utilized, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating heterogeneity within the study. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. When instrumental variables influence the occurrence of an outcome through factors other than the exposure factor, it indicates that the instrumental variables are polyvalent. pleiotropy can lead to failure of the independence and exclusivity assumptions. With MR-Egger intercept, it is possible to detect the pleiotropy of the data and to assess the robustness of the results. If p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, it indicates the presence of pleiotropy in the data [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].MR-PRESSO uses the global test to detect horizontal pleiotropy and, if necessary, to correct potential pleiotropy outliers through outlier removal.The MR Steiger test examines causality direction, while Leave-one-out analyses assess the impact of individual SNP removal on results. If the removal of a SNP does not significantly alter the outcomes, it suggests that the SNP does not have a nonspecific effect on the results. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll data analyses were performed using the R language (version 4.3.0) software packages \"Two-Sample-MR\" \"MR-PRESSO\" and \"mr.raps\" programme packages were carried out[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSelection of instrumental variables\u003c/h2\u003e \u003cp\u003eIn the European population, we screened 58 SNPs (PM2.5), 24 SNPs (PM2.5-10), 242 SNPs (PM10), 104 SNPs (nitrogen dioxide), 75 SNPs (nitrogen oxides), 21 SNPs ( workplace full of chemical or other fumes), 18 SNPs (workplace had a lot of cigarette smoke from other people smoking), 22 SNPs (workplace had a lot of diesel exhaust ), 20 SNPs (workplace very cold ), 16 SNPs (workplace very dusty ), 14 SNPs (workplace very hot ), and 18 SNPs (workplace very noisy ). In the Asian population, we screened 5 SNPs (PM2.5), 4 SNPs (PM2.5-10), 8 SNPs (PM10), 8 SNPs (nitrogen dioxide), and 5 SNPs (nitrogen oxides) from 5 exposure factors. Data for all SNPs closely related to exposure factors are in Supplementary Table\u0026nbsp;1. After calculation, the F-statistic values of all SNPs were greater than 10, indicating that the IVs had a small bias and could be analyzed by MR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCausal relationship between environmental pollution and mtDNA copy number in European populations\u003c/h2\u003e \u003cp\u003eThere was no causal relationship between the 12 exposures and mtDNA copy number in the European population. The following advantage ratios (OR) and 95% confidence intervals (CI) for various exposures were shown in our MR study: PM2.5 (OR\u0026thinsp;=\u0026thinsp;0.973, 95% CI 0.921\u0026ndash;1.029, p\u0026thinsp;=\u0026thinsp;0.341), PM2.5-10 (OR\u0026thinsp;=\u0026thinsp;0.997, 95% CI 0.912\u0026ndash;1.091, p\u0026thinsp;=\u0026thinsp;0.954), PM10 (OR\u0026thinsp;=\u0026thinsp;0.994, 95% CI 0.964\u0026ndash;1.025, p\u0026thinsp;=\u0026thinsp;0.710), nitrogen dioxide (OR\u0026thinsp;=\u0026thinsp;0.973, 95% CI 0.932\u0026ndash;1.014, p\u0026thinsp;=\u0026thinsp;0.196), nitrogen oxides (OR\u0026thinsp;=\u0026thinsp;0.983, 95% CI 0.934\u0026ndash;1.035, p\u0026thinsp;=\u0026thinsp;0.524), workplace full of chemical or other fumes Often (OR\u0026thinsp;=\u0026thinsp;0.130, 95% CI 0.930\u0026ndash;1.136, p\u0026thinsp;=\u0026thinsp;0.194), workplace had a lot of cigarette smoke from other people smoking Often (OR\u0026thinsp;=\u0026thinsp;1.012, 95% CI 0.897\u0026ndash;1.142, p\u0026thinsp;=\u0026thinsp;0.847), workplace had a lot of diesel exhaust Often (OR\u0026thinsp;=\u0026thinsp;1.058, 95% CI 0.812\u0026ndash;1.379, p\u0026thinsp;=\u0026thinsp;0.677), workplace very cold Often (OR\u0026thinsp;=\u0026thinsp;1.047, 95% CI 0.903\u0026ndash;1.215, p\u0026thinsp;=\u0026thinsp;0.541), workplace very cold Often (OR\u0026thinsp;=\u0026thinsp;1.028, 95% CI 0.847\u0026ndash;1.248, p\u0026thinsp;=\u0026thinsp;0.778), workplace very hot Often ( OR\u0026thinsp;=\u0026thinsp;0.948, 95% CI 0.793\u0026ndash;1.132, p\u0026thinsp;=\u0026thinsp;0.554), and workplace very noisy Often (OR\u0026thinsp;=\u0026thinsp;0.998, 95% CI 0.880\u0026ndash;1.131, p\u0026thinsp;=\u0026thinsp;0.973). The results of all MR analyses are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCausal relationship between environmental pollution and mtDNA copy number in East Asian populations\u003c/h2\u003e \u003cp\u003eWe explored the causal relationship between only five exposure factors (PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) and mtDNA copy number. The results showed no causal relationship between any of the five exposure factors and mtDNA copy number. The following are the odds ratios (OR) and 95% confidence intervals (CI) for the various exposures shown in our MR study: PM2.5 (OR\u0026thinsp;=\u0026thinsp;1.001, 95% CI 0.877\u0026ndash;1.142, p\u0026thinsp;=\u0026thinsp;0.990), PM2.5-10 (OR\u0026thinsp;=\u0026thinsp;0.982, 95% CI 0.887\u0026ndash;1.089, p\u0026thinsp;=\u0026thinsp;0.739), PM10 (OR\u0026thinsp;=\u0026thinsp;1.025, 95% CI 0.948\u0026ndash;1.107, p\u0026thinsp;=\u0026thinsp;0.537), nitrogen dioxide (OR\u0026thinsp;=\u0026thinsp;1.039, 95% CI 0.960\u0026ndash;1.125, p\u0026thinsp;=\u0026thinsp;0.341), and nitrogen oxides (OR\u0026thinsp;=\u0026thinsp;1.018, 95% CI 0.917\u0026ndash;1.131, p\u0026thinsp;=\u0026thinsp;0.735). The results of all MR analyses are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003esensitivity analysis\u003c/h2\u003e \u003cp\u003eHeterogeneity and multiplicity analyses were performed in both the European and Asian populations to verify the stability of the causal relationships we obtained through MR analysis. Heterogeneity analysis: no significant heterogeneity was observed in both the IVW test and the MR-Egger test. Analysis of pleiotropy: the MR-Egger intercept test showed a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05, indicating the absence of horizontal pleiotropy. PM10 (p\u0026thinsp;=\u0026thinsp;0.005) and workplace had a lot of diesel exhaust Often (p\u0026thinsp;=\u0026thinsp;0.013) with mtDNA copy number in the European population were statistically significant in MR-PRESSO. There were outliers rs62118471, and rs6447363 between PM10 and mtDNA copy numbers in the European population. after the removal of the outliers, there was still no statistical significance in the MR analysis of PM10 versus mtDNA copy number (Supplementary Material). Heterogeneity and pleiotropy analyses indicated the stability of the results (Table\u0026nbsp;2). After removing SNPs one by one, respectively, by the leave-one-out method, effect size estimation was performed for the remaining SNPs, and the results showed that there was no large difference between the effect sizes before and after removal, suggesting that no single SNP had a significant effect on the MR estimation results (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized GWAS data on environmental contamination and mtDNA copy number from public databases. It is the first Mendelian randomization study to investigate the potential causal relationship between environmental contamination and mtDNA copy number. Despite thorough quality control measures, no definitive evidence supporting a direct causal link between environmental contamination and mtDNA copy number was found. Further research is needed to explore the impact of environmental pollution on changes in mtDNA copy number, as these changes may be influenced by a combination of environmental factors and other variables rather than directly caused by environmental pollution.\u003c/p\u003e \u003cp\u003eResults from a cross-sectional study conducted among non-smoking women in rural China revealed a significant negative association between short-term exposure to indoor PM2.5 concentrations and peripheral blood mtDNA copy number levels [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Similarly, a multicenter study in China found that individuals exposed to higher mean PM2.5 concentrations in Zhuhai had lower peripheral blood mtDNA copy numbers [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, inconsistent results were reported in a repeated-measures study involving patients with type 2 diabetes, showing no short-term association between PM or gaseous pollutants and blood mtDNA copy number [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These discrepancies in findings could be attributed to variations in the susceptibility of study populations and differences in the sources and components of PM2.5. An epidemiologic study by Pieters et al. indicated that brief exposures to PM2.5 were linked to increased mtDNA copy numbers, while sustained exposures were associated with decreased mtDNA copy numbers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This phenomenon may be explained by the fact that short-term exposure to particulate matter triggers an acute inflammatory response, leading to a temporary rise in mtDNA copy number. It is suggested that this increase in mtDNA copy number might serve as an adaptive mechanism during the initial phase of exposure to harmful pollutants. However, as exposure duration increases, oxidative stress induced by particulate matter becomes dominant, resulting in damage to mitochondrial DNA and subsequent reduction in mtDNA copy number. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. An increase in mtDNA copy number due to short-term exposure to airborne particulate matter has been similarly reported in an occupational population of healthy male steelworkers [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This suggests that mtDNA copy number could potentially serve as a mediator in the relationship between air pollution and the development of chronic diseases. Previous research has shown that prenatal exposure to heavy metals such as Cd, Pb, and As can impact neonatal mtDNA copy number [\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], although findings have not been entirely consistent. For instance, while Sanchez-Guerra et al. observed a rise in neonatal cord blood mtDNA copy number associated with Pb exposure in mid- and late pregnancy [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], Smith et al. did not find a significant link between Pb exposure in early pregnancy and cord blood mtDNA copy number [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The varying conclusions of these studies may be attributed to differences in the timing of exposure assessments. Therefore, further investigation is warranted to explore the relationship between heavy metal exposure and neonatal mtDNA copy number by collecting samples at multiple time points during pregnancy and identifying the potential window of sensitivity to prenatal heavy metal exposure on neonatal mtDNA copy number.\u003c/p\u003e \u003cp\u003eEnvironmental pollution causes oxidative stress in the organism and the copy number of mtDNA in the cell changes in response to the excessive reactive oxygen species (ROS) produced by oxidative stress in the cell [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Mitochondria are the main source of ROS, and the accumulation of ROS can easily cause oxidative damage to mitochondrial DNA. mtDNA copy number changes can reflect the degree of oxidative stress and mitochondrial damage [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. mtDNA copy number increase is an effective mechanism to cope with oxidative stress, and can effectively buffer the damage caused by oxidative stress [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. When the organism undergoes elevated levels of oxidative stress, the mitochondrial repair mechanism is insufficient to remove damaged mitochondrial DNA, and at the same time the mitochondrial function is unable to meet the needs of the organism, resulting in the activation of the pathway of p53 and peroxisome proliferator-activated receptor γ coactivator-1α [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], which exhibits the elevation of the mtDNA copy number to satisfy the needs of normal cellular function. Long-term stress response of the organism leads to increased accumulation of ROS, and mitochondrial dysfunction reduces the mitochondrial content, which is manifested as a decrease in mtDNA copy number [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has several strengths. We explored the causal relationship between environmental contamination and mtDNA copy number using a two-sample Mendelian randomization study approach. This method allows for causality analysis of exposure and outcome by utilizing public GWAS data, making it more resource-efficient compared to the randomized controlled trial approach. Previous studies were limited in controlling for confounders due to the inability to conduct randomized controlled trials. Observational studies have yielded inconclusive results possibly influenced by confounding bias or causality reversal. By employing a GWAS-based MR method, we effectively mitigated confounding factors and causality reversal to analyze the causal association between environmental pollution and mtDNA copy number at the gene level.\u003c/p\u003e \u003cp\u003eHowever, it is important to acknowledge certain limitations of this study. Firstly, the analysis focused solely on the causal relationship between environmental pollution and mtDNA copy number, without exploring the potential impact of other factors. The regulation of mtDNA copy number is a multifaceted process that may be influenced by various variables, which should be considered in future research. Secondly, the GWAS data utilized in this study predominantly originated from European populations, with limited representation from Asian or other populations, potentially restricting the generalizability of the findings. Thirdly, while the MR method employed is a robust approach for causal analysis, it is essential to conduct animal experiments in the future to validate any potential causal link between environmental contamination and mtDNA copy number. Lastly, although the results of the heterogeneity and multicollinearity analyses we performed showed no obvious abnormalities, bias due to multicollinearity or exponential event bias is inevitable.\u003c/p\u003e \u003cp\u003eIn conclusion, our study did not identify a direct causal relationship between environmental pollution and mtDNA copy number. Nonetheless, the observed relationship warrants further investigation to fully understand its implications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and design: BBZ, KWL; Data collection and integration: CL, ZXDW, SQW, and XL; Data analysis and interpretation: CL,QY, ZXDW, SQW, XL,WZG; Writing of the first draft of the manuscript:BBZ; Manuscript review and editing: BBZ,DB,CL,QY, ZXDW, SQW, XL,WZG,KWL. All authors reviewed and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the IEU Open GWAS 340 project (https://gwas.mrcieu.ac.uk/datasets/) for providing summary results data for the analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarais EA, Vohra K, Kelly JM, et al. The health burden of air pollution in the UK: a modeling study using updated exposure-risk associations. lancet. 2023;402 Suppl 1:S66\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen AJ, Brauer M, Burnett R, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. lancet. 2017;389 (10082):1907\u0026ndash;1918.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSin DD, Doiron D, Agusti A, et al. Air pollution and COPD: GOLD 2023 committee report. Eur Respir J. 2023;61 (5):. Eur Respir J. 2023;61 (5):.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedrich MJ. Air Pollution Is Greatest Environmental Threat to Health. JAMA. 2018;319 (11):1085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApte JS, Marshall JD, Cohen AJ, et al. Addressing Global Mortality from Ambient PM2.5. Environ Sci Technol. 2015;49 (13):8057\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen AJ, Brauer M, Burnett R, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. lancet. 2017;389 (10082):1907\u0026ndash;1918.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLedda C, Iavicoli I, Bracci M, et al. Serum lipid, lipoprotein, and apolipoprotein profiles in workers exposed to low arsenic levels: lipid profiles and occupational arsenic exposure. Toxicol Lett. 2018;282:49\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemers PA, DeMarini DM, Fent KW, et al. Carcinogenicity of occupational exposure as a firefighter. Lancet Oncol. 2022;23 (8):985\u0026ndash;986.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Wang J, Wang H, et al. Surveillance of noise exposure level in industrial enterprises- Jiangsu Province, China, 2022. Front Public Health. 2024;12:1230481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClyde D. Mitochondrial DNA copy number and disease. Nat Rev Genet. 2022;23 (3):136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMemon AA, Vats S, Sundquist J, et al. Mitochondrial DNA Copy Number: Linking Diabetes and Cancer. Antioxid Redox Signal. 2022;37 (16\u0026ndash;18):1168\u0026ndash;1190.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong YS, Longchamps RJ, Zhao D, et al. Mitochondrial DNA Copy Number and Incident Heart Failure: The Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2020;141 (22):1823\u0026ndash;1825.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Y, Ju YS, Kim Y, et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nat Genet. 2020;52 (3):342\u0026ndash;352. Nat Genet. 2020;52 (3):342\u0026ndash;352.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiao JC, Sun LJ, Zhang MY, et al. Association between ambient particulate matter exposure and mitochondrial DNA copy number: A systematic review and meta-analysis. Sci Total Environ. 2024;923:171423.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer JN, Hartman JH, Mello DF. Mitochondrial Toxicity. Toxicol Sci. 2018;162 (1):15\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Hart JE, Liu Q, et al. Association of particulate matter air pollution with leukocyte mitochondrial DNA copy number. Environ Int. 2020;141. 105761.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Li S, Pan M, et al. Physical activity counteracted associations of exposure to mixture of air pollutants with mitochondrial DNA copy number among rural Chinese adults. Chemosphere. 2021;272:129907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu C, Sheng X, Li Y, et al. Effects of prenatal exposure to particulate air pollution on newborn mitochondrial DNA copy number. chemosphere. 2020;253:126592.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosa MJ, Just AC, Guerra MS, et al. Identifying sensitive windows for prenatal particulate air pollution exposure and mitochondrial DNA content in cord blood. environ Int. 2017;98:198\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data.Genet Epidemiol. 2013;37 (7):658\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai X, Liang C, Zhang M, et al. Mitochondrial DNA copy number and cancer risks: a comprehensive Mendelian randomization analysis. Int J Cancer. 2024;. 154 (8):1504\u0026ndash;1513.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin P, Qin T, Liang L, et al. The role of mitochondrial DNA copy number in cardiometabolic disease: a bidirectional two-sample mendelian randomization study. Cardiovasc Diabetol. 2024;23 (1):45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong MR, Narula S, Morton R, et al. Mitochondrial DNA Copy Number as a Marker and Mediator of Stroke Prognosis: Observational and Mendelian Randomization Analyses. Neurology. 2021;:.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu TQ, Xu WT, Su YN, et al.Basic principles, methods, and limitations of Mendelian randomization research[J]. Journal of Evidence-Based Medicine, 2021,21(10):1227\u0026ndash;1234.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEeftens M, Beelen R, de Hoogh K, et al. Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) in 20 European study areas; results of the ESCAPE project. Environ Sci Technol. 2012;46 (20):11195\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. nature. 2018;562 (7726):203\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong M, Mohammadi-Shemirani P, Perrot N, et al. GWAS and ExWAS of blood mitochondrial DNA copy number identify 71 loci and highlights a potential causal role in dementia. Elife. 2022;11:. Elife. 2022;11:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue Y, Zhang L, Chen Y, et al. Gut microbiota and atopic dermatitis: a two-sample Mendelian randomization study. Front Med (Lausanne). 2023;10:1174331.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai J, Li X, Wu S, Tian Y, Zhang Y, Wei Z, et al. Assessing the causal association between human blood metabolites and the risk of epilepsy. J Transl Med (2022) 20(1):437.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartwig FP, Davies NM, Hemani G, et al. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol. 2016;45 (6):1717\u0026ndash;1726.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG, Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40 (3):755\u0026ndash;64. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyr036\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyr036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng R, Lu M, Xu J, et al. Pulmonary embolism and 529 human blood metabolites: genetic correlation and two-sample Mendelian randomization study. BMC Genom Data. 2022;23 (1):69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTTachmazidou I, Hatzikotoulas K, Southam L, et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet. 2019;51 (2):230\u0026ndash;236.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Bowden J, Fall T, et al. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. epidemiology. 2017;28 (1):30\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlob EA, Groenen PJ, Thurik AR, et al. A note on the use of Egger regression in Mendelian randomization studies [JJ. Int J Epidemiol, 2017, 46(6) -2094- 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlob EAW, Groenen PJF, Thurik AR, et al. A note on the use of Egger regression in Mendelian randomization studies. Int J Epidemiol. 2017;46 (6):2094\u0026ndash;2097.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50 (5):693\u0026ndash;698.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco M FD, Minelli C, Sheehan NA, et al. Detecting pleiotropy in Mendelian randomization studies with summary data and a continuous outcome. stat. Med. 2015;34 (21):2926\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44 (2):512\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani G,Zheng J,Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong JYY, Hu W, Downward GS, et al. Personal exposure to fine particulate matter and benzo[a]pyrene from indoor air pollution and leukocyte mitochondrial DNA copy number in rural China. Carcinogenesis. 2017;38 (9):893\u0026ndash;899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Zhu M, Du J, et al. Genetic variants in nuclear DNA along with environmental factors modify mitochondrial DNA copy number: a population-based exome-wide association study. BMC Genomics. 2018;19 (1):752.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia Y,Chen R,Wang C, et al. Ambient air pollution, blood mitochondrial DNA copy number, and telomere length in a panel of diabetes patients. Inhal Toxicol. 2015;27 (10):481\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePieters N, Janssen BG, Dewitte H, et al. Biomolecular Markers within the Core Axis of Aging and Particulate Air Pollution Exposure in the Elderly: A Cross-Sectional Study. Environ Health Perspect. 2016;124 (7):943\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGustafsson CM,Falkenberg M,Larsson NG. Maintenance and Expression of Mammalian Mitochondrial DNA. Annu Rev Biochem. 2016;85:133\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByun HM, Baccarelli AA. Environmental exposure and mitochondrial epigenetics: study design and analytical challenges. Hum Genet. 2014;133 (3). 247\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou L, Zhu ZZ, Zhang X, et al. Airborne particulate matter and mitochondrial damage: a cross-sectional study. Environ Health. 2010;9:48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVriens A, Nawrot TS, Baeyens W, et al. Neonatal exposure to environmental pollutants and placental mitochondrial DNA content: a multi-pollutant approach. environ Int. 2017;106:60\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong L, Liu B, Wang L, et al. Exposure to arsenic during pregnancy and newborn mitochondrial DNA copy number: a birth cohort study in Wuhan, China. Chemosphere. 2020;243:125335.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Jia DY, Cai SZ, et al. Mitochondria defects are involved in lead-acetate-induced adult hematopoietic stem cell decline. Toxicol Lett. 2015;. 235 (1):37\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik AN, Czajka A. Is mitochondrial DNA content a potential biomarker of mitochondrial dysfunction? Mitochondrion. 2013;13 (5):481\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer JN, Leung MC, Rooney JP, et al. Mitochondria as a target of environmental toxicants. Toxicol Sci. 2013;134 (1):1\u0026ndash;17. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/toxsci/ kft102\u003c/span\u003e\u003cspan address=\"10.1093/toxsci/ kft102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen S, Gao J, Zhang L, et al. p53 increase mitochondrial copy number via up-regulation of mitochondrial transcription factor A in colorectal cancer. Oncotarget. 2016;7 (46):75981\u0026ndash;75995.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabrowska A,Venero JL, Iwasawa R, et al. PGC-1α controls mitochondrial biogenesis and dynamics in lead-induced neurotoxicity. Aging (Albany NY). 2015;7 (9):629\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClay Montier LL, Deng JJ, Bai Y. Number matters control of mammalian mitochondrial DNA copy number. J Genet Genomics. 2009;36 (3):125\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental pollution, Air pollution, Mitochondrial DNA copy number, Mendelian randomization, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-4506104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4506104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eIn recent years, the incidence of diseases associated with environmental pollution has increased dramatically worldwide. Previous studies have shown that mitochondrial DNA (mtDNA) copy number is a potential biomarker for diseases caused by environmental pollution, and therefore, the causal relationship between environmental pollution and mtDNA copy number needs to be further explored.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eWe performed Mendelian randomization analyses of European and Asian populations using a large amount of publicly available genome-wide association study (GWAS) pooled data. Genetic loci that are independent of each other and strongly associated with environmental pollution were selected as instrumental variables, and the inverse variance weighting (IVW) method was used as the primary analytical method. Cochrane's Q-test was used to assess heterogeneity. Multiplicity was checked using MR-Egger regression test.MR-PRESSO method was used to identify outliers. Sensitivity analysis was performed using leave-one-out. The results were assessed based on effect indicator dominance ratio (OR) and 95% confidence interval (CI).\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eIn the European population, genetically predicted PM2.5 (p\u0026thinsp;=\u0026thinsp;0.341), PM2.5-10 (p\u0026thinsp;=\u0026thinsp;0.954), PM10 (p\u0026thinsp;=\u0026thinsp;0.710), nitrogen dioxide (p\u0026thinsp;=\u0026thinsp;0.196), nitrogen oxides (p\u0026thinsp;=\u0026thinsp;0.524), workplace full of chemical or other fumes (p\u0026thinsp;=\u0026thinsp;0.194), workplace with a lot of cigarette smoke from other people smoking (p\u0026thinsp;=\u0026thinsp;0.847), workplace had a lot of diesel exhaust (p\u0026thinsp;=\u0026thinsp;0.677), workplace very cold (p\u0026thinsp;=\u0026thinsp;0.541), workplace very cold (p\u0026thinsp;=\u0026thinsp;0.778), workplace very hot (p\u0026thinsp;=\u0026thinsp;0.554), and workplace very noisy (p\u0026thinsp;=\u0026thinsp;0.973) were not associated with risk of mtDNA copy number. In the Asian population, genetically predicted PM2.5 (p\u0026thinsp;=\u0026thinsp;0.990), PM2.5-10 (p\u0026thinsp;=\u0026thinsp;0.739), PM10 (p\u0026thinsp;=\u0026thinsp;0.537), nitrogen dioxide (p\u0026thinsp;=\u0026thinsp;0.341), and nitrogen oxides (p\u0026thinsp;=\u0026thinsp;0.735) were not associated with the risk of mtDNA copy number. Sensitivity analysis proved the stability of the results.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eThe results of this Mendelian randomization do not support a causal relationship between environmental pollution and mtDNA copy number. However, the causal relationship found in this study still needs to be further explored.\u003c/p\u003e","manuscriptTitle":"Relationship between environmental pollution and mitochondrial DNA copy number in European and East Asian populations: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 08:50:40","doi":"10.21203/rs.3.rs-4506104/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"180fde63-1080-4f79-ad0c-f50354d478d4","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32802775,"name":"Health sciences/Medical research/Genetics research"},{"id":32802776,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2024-07-19T07:48:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 08:50:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4506104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4506104","identity":"rs-4506104","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}