Causal relationship between particulate matter and COVID-19 risk: A mendelian randomization study
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ABSTRACT:
SUBMITTER: Zhu J
PROVIDER: S-EPMC10909784 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
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Project description:BackgroundsMany studies have shown particulate matter has emerged as one of the major environmental risk factors for diabetes; however, studies on the causal relationship between particulate matter 2.5 (PM2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM2.5 using two sample mendelian randomization (TSMR).MethodsWe collected genetic data from European ancestry publicly available genome wide association studies (GWAS) summary data through the MR-BASE repository. The IEU GWAS information output PM2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship of PM2.5 (2010) were modeled for each address using a Land Use Regression model developed as part of the European Study of Cohorts for Air Pollution Effects. Diabetes GWAS information (Consortium = MRC-IEU, sample size: 461,578) were used, and the genetic variants were used as the instrumental variables (IVs). We performed three representative Mendelian Randomization (MR) methods: Inverse Variance Weighted regression (IVW), Egger, and weighted median for causal relationship using genetic variants. Furthermore, we used a novel method called MR Mixture to identify outlier SNPs.ResultsFrom the IVW method, we revealed the causal relationship between PM2.5 and diabetes (Odds ratio [OR]: 1.041, 95% CI: 1.008-1.076, P = 0.016), and the finding was substantiated by the absence of any directional horizontal pleiotropy through MR-Egger regression (β = 0.016, P = 0.687). From the IVW fixed-effect method (i.e., one of the MR machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC = 0.72) with a causal relationship between PM2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, P = 0.012).ConclusionWe identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods.
| S-EPMC10450337 | biostudies-literature
Project description:BackgroundEpidemiological surveys have found that particulate matter 2.5 (PM2.5) plays an important role in hypothyroidism. However, due to the methodological limitations of traditional observational studies, it is difficult to make causal inferences. In the present study, we assessed the causal association between PM2.5 concentrations and risk of hypothyroidism using two-sample Mendelian randomization (TSMR).MethodsWe performed TSMR by using aggregated data from genome-wide association studies (GWAS) on the IEU Open GWAS database. We identified seven single nucleotide polymorphisms (SNPs) associated with PM2.5 concentrations as instrumental variables (IVs). We used inverse-variance weighting (IVW) as the main analytical method, and we selected MR-Egger, weighted median, simple model, and weighted model methods for quality control.ResultsMR analysis showed that PM2.5 has a positive effect on the risk of hypothyroidism: An increase of 1 standard deviation (SD) in PM2.5 concentrations increases the risk of hypothyroidism by ~10.0% (odds ratio 1.10, 95% confidence interval 1.06-1.13, P = 2.93E-08, by IVW analysis); there was no heterogeneity or pleiotropy in the results.ConclusionIn conclusion, increased PM2.5 concentrations are associated with an increased risk of hypothyroidism. This study provides evidence of a causal relationship between PM2.5 and the risk of hypothyroidism, so air pollution control may have important implications for the prevention of hypothyroidism.
| S-EPMC9732245 | biostudies-literature
Project description:BackgroundPrevious observational studies suggested a correlation between particulate matter 2.5 (PM2.5) and infectious diseases, but causality remained uncertain. This study utilized Mendelian randomization (MR) analysis to investigate causal relationships between PM2.5 concentrations and various infectious diseases (COVID-19 infection, hospitalized COVID-19, very severe COVID-19, urinary tract infection, bacterial pneumonia, and intestinal infection).MethodsInverse variance weighted (IVW) was the primary method for evaluating causal associations. For significant causal estimates, multiple sensitivity tests were further performed: (i) three additional MR methods (MR-Egger, weighted median, and maximum likelihood method) for supplementing IVW; (ii) Cochrane's Q test for assessing heterogeneity; (iii) MR-Egger intercept test and MR-PRESSO global test for evaluating horizontal pleiotropy; (iv) leave-one-out sensitivity test for determining the stability.ResultsPM2.5 concentration significantly increased the risk of hospitalized COVID-19 (OR = 1.91, 95 % CI: 1.06-3.45, P = 0.032) and very severe COVID-19 (OR = 3.29, 95 % CI: 1.48-7.35, P = 3.62E-03). However, no causal effect was identified for PM2.5 concentration on other infectious diseases (P > 0.05). Furthermore, various sensitivity tests demonstrated the reliability of significant causal relationships.ConclusionsOverall, lifetime elevated PM2.5 concentration increases the risk of hospitalized COVID-19 and very severe COVID-19. Therefore, controlling air pollution may help mitigate COVID-19 progression.
| S-EPMC10755308 | biostudies-literature
Project description:Background & aimsCoronavirus disease 2019 (COVID-19) is strongly associated with myocarditis or pericarditis risk in observational studies, however, there are still studies that do not support the above conclusion. Whether the observed association reflects causation needs to be confirmed. We performed a bidirectional Mendelian randomization (MR) study to assess the causal relationship of COVID-19, which was divided into three groups, namely severe COVID-19, hospitalized COVID-19, and COVID-19 infection, measured by myocarditis or pericarditis.MethodsWe extracted summary genome-wide association statistics for the severe COVID-19 (case: 13,769, control: 1,072,442), hospitalized COVID-19 (case: 32,519, control: 2,062,805), COVID-19 infection (case: 122,616, control: 2,475,240), myocarditis (case 1,521, control 191,924), and pericarditis (case 979, control 286,109) among individuals of European ancestry. Independent genetic variants that exhibited a significant association with each phenotype at the genome-wide level of significance were utilized as instrumental variables. Estimation of the causal effect was mainly performed using the random effects inverse-variance weighted method (IVW). Additionally, other tests such as MR-Egger intercept, MR-PRESSO, Cochran's Q-test, "Leave-one-out", and funnel plots were conducted to assess the extent of pleiotropy and heterogeneity.ResultsNon-associations in the IVW and sensitivity analyses were observed for COVID-19 with myocarditis or pericarditis. Severe COVID-19 was not associated with myocarditis [odds ratio (OR), 1.00; 95% confidence interval (CI), 0.89-1.12; P = 0.99], pericarditis (OR = 0.90, 95% CI, 0.78-1.04, P = 0.17). Similar results can be observed in hospitalized COVID-19, and COVID-19 infection. At the same time, null associations were observed for myocarditis or pericarditis with COVID-19 traits in the reverse direction. The main results are kept stable in the sensitivity analysis.ConclusionThere is no evidence that COVID-19 is independently and causally associated with myocarditis or pericarditis.
| S-EPMC10755931 | biostudies-literature
Project description:ObjectiveCOVID-19 is a highly transmissible disease that can result in long-term symptoms, including chronic pain. However, the mechanisms behind the persistence of long-COVID pain are not yet fully elucidated, highlighting the need for further research to establish causality. Mendelian randomization (MR), a statistical technique for determining a causal relationship between exposure and outcome, has been employed in this study to investigate the association between COVID-19 and chronic pain.Material and methodsThe IVW, MR Egger, and weighted median methods were employed. Heterogeneity was evaluated using Cochran's Q statistic. MR Egger intercept and MR-PRESSO tests were performed to detect pleiotropy. The Bonferroni method was employed for the correction of multiple testing. R software was used for all statistical analyses.ResultBased on the IVW method, hospitalized COVID-19 patients exhibit a higher risk of experiencing lower leg joint pain compared to the normal population. Meanwhile, the associations between COVID-19 hospitalization and back pain, headache, and pain all over the body were suggestive. Additionally, COVID-19 patients requiring hospitalization were found to have a suggestive higher risk of experiencing neck or shoulder pain and pain all over the body compared to those who did not require hospitalization. Patients with severe respiratory-confirmed COVID-19 showed a suggestive increased risk of experiencing pain all over the body compared to the normal population.ConclusionOur study highlights the link between COVID-19 severity and pain in different body regions, with implications for targeted interventions to reduce COVID-19 induced chronic pain burden.
| S-EPMC10798446 | biostudies-literature
Project description:BackgroundThe 2019 coronavirus disease pandemic (COVID-19) poses an enormous threat to public health worldwide, and the ensuing management of social isolation has greatly decreased opportunities for physical activity (PA) and increased opportunities for leisure sedentary behaviors (LSB). Given that both PA and LSB have been established as major influencing factors for obesity, diabetes and cardiometabolic syndrome, whether PA/LSB in turn affects the susceptibility to COVID-19 by disrupting metabolic homeostasis remains to be explored. In this study, we aimed to systematically evaluate the causal relationship between PA/LSB and COVID-19 susceptibility, hospitalization and severity using a Mendelian randomization study.MethodsData were obtained from a large-scale PA dataset (N = 377,000), LSB dataset (N = 422,218) and COVID-19 Host Genetics Initiative (N = 2,586,691). The causal effects were estimated with inverse variance weighted, MR-Egger, weighted median and MR-PRESSO. Sensitivity analyses were implemented with Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis and the funnel plot. Risk factor analyses were further conducted to investigate the potential mediators.ResultsGenetically predicted accelerometer-assessed PA decreased the risk for COVID-19 hospitalization (OR = 0.93, 95% CI 0.88-0.97; P = 0.002), while leisure television watching significantly increased the risk of COVID-19 hospitalization (OR = 1.55, 95% CI 1.29-1.88; P = 4.68 × 10-6) and disease severity (OR = 1.85, 95% CI 1.33-2.56; P = 0.0002) after Bonferroni correction. No causal effects of self-reported moderate to vigorous physical activity (MVPA), accelerometer fraction of accelerations > 425 milligravities, computer use or driving on COVID-19 progression were observed. Risk factor analyses indicated that the above causal associations might be mediated by several metabolic risk factors, including smoking, high body mass index, elevated serum triglyceride levels, insulin resistance and the occurrence of type 2 diabetes.ConclusionOur findings supported a causal effect of accelerometer-assessed PA on the reduced risk of COVID-19 hospitalization as well as television watching on the increased risk of COVID-19 hospitalization and severity, which was potentially mediated by smoking, obesity and type 2 diabetes-related phenotypes. Particular attention should be given to reducing leisure sedentary behaviors and encouraging proper exercise during isolation and quarantine for COVID-19.
| S-EPMC9100292 | biostudies-literature
Project description:BackgroundSeveral observational studies demonstrated that pregnant individuals with COVID-19 had a higher risk of preeclampsia and preterm birth. We aimed to determine whether women with COVID-19 diagnosis had adverse pregnancy outcomes.MethodsA two-sample Mendelian randomization (MR) analysis in this study was used to evaluate the casual relationships between COVID-19 infection and obstetric-related diseases based on genome-wide association studies (GWAS) dataset. Inverse-variance weighted (IVW), MR-Egger and MR-PRESSO were used to infer the connection and estimate the pleiotropy respectively.ResultsThe significant connection was observed between COVID-19 and placental disorders with betaIVW of 1.57 and odds ratio (OR) of 4.81 (95% confidence interval [CI]: 1.05-22.05, p = 0.04). However, there were no associations between COVID-19 infection and gestational diabetes mellitus (GDM) (OR = 1.12; 95% CI: 0.85-1.45, p = 0.41), other disorders of amniotic fluid and membranes (OR = 0.90; 95% CI: 0.61-1.32, p = 0.59), Intrahepatic Cholestasis of Pregnancy (ICP) (OR = 1.42; 95% CI: 0.85-2.36, p = 0.18), birth weight (OR = 1.02; 95% CI: 0.99-1.05, p = 0.19), gestational hypertension (OR = 1.00; 95% CI: 1.00-1.00, p = 0.85), spontaneous miscarriages (OR = 1.00; 95% CI: 0.96-1.04, p = 0.90) and stillbirth (OR = 1.00; 95% CI: 0.98-1.01, p = 0.62).ConclusionThere was no direct causal relationship between COVID-19 infection and maternal and neonatal poor outcomes. Our study could alleviate the anxiety of pregnant women under the COVID-19 pandemic conditions partly.
| S-EPMC10964700 | biostudies-literature
Project description:BackgroundSeveral observational studies reported on the association between particulate matter ≤2.5μm (PM2.5) and its absorbance with coronavirus (COVID-19), but none use Mendelian randomisation (MR). To strengthen the knowledge on causality, we examined the association of PM2.5 and its absorbance with COVID-19 risk using MR.MethodsWe selected genome-wide association study (GWAS) integration data from the UK Biobank and IEU Open GWAS Project for two-sample MR analysis. We used inverse variance weighted (IVW) and its multiple random effects and fixed effects alternatives to generally predict the association of PM2.5 and its absorbance with COVID-19, and six methods (MR Egger, weighted median, simple mode, weighted mode, maximum-likelihood and MR-PRESSO) as complementary analyses.ResultsMR results suggested that PM2.5 absorbance was associated with COVID-19 infection (odds ratio (OR) = 2.64; 95% confidence interval (CI) = 1.32-5.27, P = 0.006), hospitalisation (OR = 3.52; 95% CI = 1.05-11.75, P = 0.041) and severe respiratory symptoms (OR = 28.74; 95% CI = 4.00-206.32, P = 0.001) in IVW methods. We observed no association between PM2.5 and COVID-19.ConclusionsWe found a potential causal association of PM2.5 absorbance with COVID-19 infection, hospitalisation, and severe respiratory symptoms using MR analysis. Prevention and control of air pollution could help delay and halt the negative progression of COVID-19.
| S-EPMC10346132 | biostudies-literature
Project description:Recent studies have shown that gut microbiota is associated with coronavirus disease 2019 (COVID-19). However, the causal impact of the gut microbiota on COVID-19 remains unclear. We performed a bidirectional Mendelian randomization. The summary statistics on the gut microbiota from the MiBioGen consortium. Summary statistics for COVID-19 were obtained from the 6th round of the COVID-19 Host Genetics Initiative genome-wide association study meta-analysis. Inverse variance weighting was used as the main method to test the causal relationship between gut microbiota and COVID-19. Reverse Mendelian randomization analysis was performed. Mendelian randomization analysis showed that Intestinimas.id.2062 was associated with an increased risk of severe COVID-19. Bifidobacterium.id.436, LachnospiraceaeUCG010.id.11330, RikenellaceaeRC9gutgroup.id.11191 increase the risk of hospitalized COVID-19. RuminococcaceaeUCG014.id.11371 shows the positive protection on hospitalized COVID-19. There is no causal relationship between gut microbiota and infection with COVID-19. According to the results of reverse Mendelian randomization analysis, no significant causal effect of COVID-19 on gut microbiota was found. The study found that gut microbiota with COVID-19 has a causal relationship. This study provides a basis for the theory of the gut-lung axis. Further randomized controlled trials are needed to clarify the protective effect of probiotics against COVID-19 and the specific protective mechanisms. This study has important implications for gut microbiota as a nondrug intervention for COVID-19.
| S-EPMC10843424 | biostudies-literature
Project description:ObjectiveObservational studies have shown an association between COVID-19 and epilepsy. However, causality remains unproven. This study aimed to investigate the causative effect of genetically predicted COVID-19 phenotypes on epilepsy risk using a two-sample Mendelian randomization (MR) analysis.MethodsWe retrieved summary-level datasets for three COVID-19 phenotypes (COVID-19 susceptibility, COVID-19 hospitalization, and COVID-19 severity) and epilepsy from the genome-wide association studies conducted by the COVID-19 Host Genetics Initiative (COVID-19 HGI) and International League Against Epilepsy (ILAE) consortium, respectively. To analyze the final results, nine MR analytic methods were utilized. The inverse-variance weighted (IVW) method was chosen as the primary approach for data analysis to evaluate the potential causal effect. Other MR analytic methods (MR-Egger regression, weighted median estimator, mode based-estimator, and MR-PRESSO) were used as a supplement to IVW to ensure the robustness of the results.ResultsThe IVW approach demonstrated no causal association between any genetically predicted COVID-19 phenotype and the risk of epilepsy [COVID-19 susceptibility: odds ratio (OR) = 0.99, 95% confidence interval (CI) = 0.86-1.14, p = 0.92; COVID-19 hospitalization: OR = 1.00, 95% CI = 0.96-1.04, p = 0.95; COVID-19 severity: OR = 0.99, 95% CI = 0.96-1.01, p = 0.25]. Other MR complementary methods revealed consistent results. Additionally, no evidence for heterogeneity and horizontal pleiotropy was found.SignificanceThis MR study revealed no genetically predicted causal relationship between COVID-19 phenotypes and epilepsy.
| S-EPMC10690698 | biostudies-literature