Causal Associations of PM2.5 and GDM: A Two-Sample Mendelian Randomization Study

Are cathepsins a risk factor for papillary thyroid carcinoma? A bidirectional two-sample mendelian randomization analysis

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common type of endocrine tumor, and its incidence is on the rise. Observational studies have linked cathepsins, an endolysosomal cysteine protein hydrolase, to the malignant progression of several tumors, including PTC. However, the causal relationship between cathepsins and PTC remains unclear. The purpose of this study was to investigate the causal relationship between cathepsins and PTC using a bidirectional two-sample Mendelian randomization (MR) analysis.

METHODS

Publicly available databases were used to obtain data on cathepsins and PTCs. Single nucleotide polymorphisms were screened for instrumental variables. Causality was evaluated using five methods. Heterogeneity and sensitivity analyses were performed to evaluate the stability of the results.

RESULTS

The analysis revealed a significant association between cathepsin Z (CTSZ) and the risk of PTC (IVW, OR = 1.170, 95% CI: 1.035-1.102, P = 0.011). However, no association was found in the inverse analysis (IVW, OR = 1.006, 95% CI: 0.982-1.031, P = 0.612). The stability and reliability of the results of this study were indicated by both heterogeneity and sensitivity.

CONCLUSIONS

This study confirmed the association between CTSZ and an increased risk of PTC. This finding has important implications for clinical practice, as it may help to predict and screen for PTC at an early stage, as well as provide some guidance for therapeutic strategies against CTSZ.

Genetic variations in IGF2BP2 and CAPN10 and their interaction with environmental factors increase gestational diabetes mellitus risk in Chinese women

AIM

This study aims to investigate the association of the genetic variations in IGF2BP2 and CAPN10 as well as gene-environment interactions with the risk of gestational diabetes (GDM) in Chinese women.

MATERIALS AND METHODS

A total of 1,566 pregnant Chinese women participated in this case-control study. We employed targeted next-generation sequencing to analyze specific SNPs in IGF2BP2 (rs11927381, rs1470579, rs4402960, rs7640539) and CAPN10/rs2975760. Various genetic models were used to assess the associations of these polymorphisms with GDM risk. Gene-gene and gene-environment interactions were examined using GMDR to identify interaction models, Subsequently, logistic regression was employed to confirm the significance of these models and to evaluate their impact on GDM susceptibility.

RESULTS

Our study identified significant associations between the C allele of IGF2BP2/rs11927381 and an increased GDM susceptibility in both dominant (P = 0.031, OR = 1.247) and heterozygote (P = 0.043, OR = 1.239) gene models. Conversely, the heterozygote TC genotype of CAPN10/rs2975760 was associated with a reduced risk of GDM (P = 0.046, OR = 0.766). Increased BMI and O3 levels were linked to a higher GDM susceptibility. We discovered interactions between CAPN10/rs2975760 CC and IGF2BP2/rs11927381 TC genotype that exacerbated GDM risk (P = 0.022, OR = 11.337). Furthermore, interactions between IGF2BP2/rs11927381 and environmental factors were observed, indicating increased GDM risks (BMI: P = 0.004, OR = 1.011; O3: P = 0.013, OR = 1.002; PM2.5: P = 0.042, OR = 1.005；BC: P = 0.048, OR = 1.094; NO3-:P = 0.045, OR = 1.024).

CONCLUSION

GDM is significantly associated with IGF2BP2/rs11927381 and CAPN10/rs2975760 polymorphisms as well as exposure to O3. Furthermore, the interaction between the CAPN10/rs2975760 CC genotype and IGF2BP2/rs11927381 TC genotype, as well as environmental factors (O3, PM2.5, BMI), significantly increases the risk of GDM in Chinese women.

Comprehensive Analysis Reveals the Potential Diagnostic Value of Biomarkers Associated With Aging and Circadian Rhythm in Knee Osteoarthritis

OBJECTIVE

Knee osteoarthritis (KOA) is characterized by structural changes. Aging is a major risk factor for KOA. Therefore, the objective of this study was to examine the role of genes related to aging and circadian rhythms in KOA.

METHODS

This study identified differentially expressed genes (DEGs) by comparing gene expression levels between normal and KOA samples from the GEO database. Subsequently, we intersected the DEGs with aging-related circadian rhythm genes to obtain a set of aging-associated circadian rhythm genes differentially expressed in KOA. Next, we conducted Mendelian randomization (MR) analysis, using the differentially expressed aging-related circadian rhythm genes in KOA as the exposure factors, their SNPs as instrumental variables, and KOA as the outcome event, to explore the causal relationship between these genes and KOA. We then performed Gene Set Enrichment Analysis (GSEA) to investigate the pathways associated with the selected biomarkers, conducted immune infiltration analysis, built a competing endogenous RNA (ceRNA) network, and performed molecular docking studies. Additionally, the findings and functional roles of the biomarkers were further validated through experiments on human cartilage tissue and cell models.

RESULTS

A total of 75 differentially expressed aging-circadian rhythm related genes between the normal group and the KOA group were identified by difference analysis, primarily enriched in the circadian rhythm pathway. Two biomarkers (PFKFB4 and DDIT4) were screened by MR analysis. Then, immune infiltration analysis showed significant differences in three types of immune cells (resting dendritic cells, resting mast cells, and M2 macrophages), between the normal and KOA groups. Drug prediction and molecular docking results indicated stable binding of PFKFB4 to estradiol and bisphenol_A, while DDIT4 binds stably to nortriptyline and trimipramine. Finally, cell lines with stable expression of the biomarkers were established by lentiviral infection and resistance screening, Gene expression was significantly elevated in overexpressing cells of PFKFB4 and DDIT4 and reversed the proliferation and migration ability of cells after IL-1β treatment.

CONCLUSIONS

Two diagnostic and therapeutic biomarkers associated with aging-circadian rhythm in KOA were identified. Functional analysis, molecular mechanism exploration, and experimental validation further elucidated their roles in KOA, offering novel perspectives for the prevention and treatment of the disease.

Association between air pollution and osteoporosis: A Mendelian randomization study

Osteoporosis (OP) is a significant disease in the aging society, which poses a threat to the physical well-being of older adults. Some studies suggest that air pollution may contribute to an increased incidence of OP; however, this causal relationship has not been firmly established. To address this gap, we conducted Mendelian randomization (MR) analysis to assess the potential causal association between air pollution (including nitrogen dioxide [N = 456,380], nitrogen oxides [N = 456,380], particulate matter [PM]2.5 [N = 423,796], and PM10 [N = 455,314]) and total-body bone mineral density (BMD) (N = 56,284). We utilized summary data from IEU Open GWAS on the database of genome-wide association studies (GWAS) and employed inverse variance weighting (IVW) as our primary analytical approach. The findings from our MR study in the European population using the IVW method indicated a potential causal link between nitrogen oxides: β = -0.59, confidence interval (CI) = (-1.03 to -0.16), P = 0.008; PM2.5: β = -0.60, CI = (-1.12 to -0.08), P = .025. These results suggest that there might be a causative relationship between nitrogen oxides, PM2.5, and BMD with regards to OP development among individuals exposed to air pollution. Importantly, the observed associations passed all statistical tests without any evidence of heterogeneity or pleiotropy. Furthermore, the presence of air pollution was found to be associated with an elevated risk of developing OP. This study provides compelling evidence for a causal connection between nitrogen oxides, PM2.5, and OP, suggesting that reducing air pollution could play a crucial role in preventing OP development.

Ozone Exposure and Gestational Diabetes in Twin Pregnancies: Exploring Critical Windows and Synergistic Risks

The relationship between ozone (O3) exposure and gestational diabetes mellitus (GDM) in twin pregnancies remains unexplored. This study aimed to investigate the association between O3 exposure and GDM risk in twin pregnancies, and to explore the synergistic effects of O3 exposure with other maternal factors. A total of 428 pregnancies recruited from a prospective twin cohort were included. Cox proportional hazard models with distributed lag non-linear models (DLNMs) were applied to examine the associations between O3 exposure and the risk of GDM and to identify the critical windows. The multiplicative and additive interaction were further analyzed to test the synergistic effects. A 10 μg/m3 increase in average O3 exposure during the 12 weeks before pregnancy was associated with a 26% higher risk of GDM. The critical windows were identified in the period from the 3rd week before gestation to the 2nd gestational week as well as from the 17th to 19th gestational week. There were synergistic effects between high O3 exposure during preconception and advanced maternal age, and a history of preterm birth/abortion/stillbirth. Periconceptional O3 exposure could increase the risk of GDM in twin pregnancy women, and the synergism of O3 exposure with certain GDM risk factors was observed.

Genetic evidence for the causal effects of air pollution on the risk of respiratory diseases

BACKGROUND

Epidemiological studies have consistently demonstrated a robust association between long-term exposure to air pollutants and respiratory diseases. However, establishing causal relationships remains challenging due to residual confounding in observational studies. In this study, Mendelian randomization (MR) analysis was used to explore the causal and epigenetic relationships between various air pollutants and common respiratory diseases.

METHODS

We utilized a two-sample Mendelian randomization (TSMR) approach to explore the impact of PM2.5, PM2.5-10, PM10, NO2, and NOX on the incidence of nine respiratory diseases using data from large-scale European GWAS datasets (N = 423,796-456,380 for exposures; N = 162,962-486,484 for outcomes). The primary analytical method was inverse variance weighting (IVW), which explored the exposure-outcome relationship using single nucleotide polymorphisms (SNPs) associated with air pollution. Sensitivity analyses, including MR-Egger regression and leave-one-out analyses, were employed to ensure result consistency. Multivariate MR (MVMR) was performed to adjust for potential smoking-related confounders, such as cigarettes per day, household smoking, exposure to tobacco smoke at home, ever smoked, second-hand smoke, smoking initiation, and age at smoking initiation, as well as the independent effects of each air pollutant. Additionally, methylation and enrichment analyses were conducted to further elucidate the potential effects of air pollution on respiratory diseases.

RESULTS

TSMR analysis revealed that exposure to PM2.5 increased the risk of early-onset chronic obstructive pulmonary disease (COPD), pneumonia, pulmonary embolism and lung cancer. PM2.5-10 exposure was associated with an increased risk of lung cancer, while PM10 exposure increased the risk of pneumonia and bronchiectasis. NO2 exposure was associated with increased risks of lung cancer and adult asthma. Importantly, these associations remained robust even after controlling for potential tobacco-related confounders in the MVMR analyses. In the MVMR analysis adjusting for other pollutants, significant associations persisted between PM2.5 and early-onset COPD, and between PM10 and pneumonia. Genetic co-localization analyses confirmed that methylation of PM2.5-associated CpG loci (cg11386376 near c1orf175, cg11846064 near rfx2, cg18612040 near rptor, and cg19765378 near c7orf50) was associated with an increased risk of early-onset COPD. Finally, SNPs significantly associated with exposure and outcome were selected for enrichment analysis.

CONCLUSIONS

Our findings suggest that exposure to air pollutants may play a causal role in the development of respiratory diseases, with a potential role of epigenomic modifications emphasized. Strengthening comprehensive air pollution regulations by relevant authorities could potentially mitigate the risk of these diseases.

The causal relationship between bacterial pneumonia and diabetes: a two-sample mendelian randomization study

BACKGROUND

Previous observational studies have established the high prevalence of bacterial pneumonia in diabetic patients, which in turn leads to increased mortality. However, the presence of a causal connection between bacterial pneumonia and diabetes remains unobserved.

METHODS

We chose genome-wide significant (Ρ < 1 × 10-5 and Ρ < 1 × 10-6) and independent (r2 < 0.001) single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to proceed a bidirectional two-sample MR study. The extracted SNPs explored the relationship between bacterial pneumonia and diabetes by Inverse variance weighted (IVW), MR-Egger, and weighted median methods. In addition, we conducted the Heterogeneity test, the Pleiotropy test, MR-presso and the Leave-one-out (LOO) sensitivity test to validate the reliability of results.

RESULTS

In an MR study with bacterial pneumonia as an exposure factor, four different types of diabetes as outcome. It was observed that bacterial pneumonia increases the incidence of GDM (OR = 1.150 (1.027-1.274, P = 0.011) and T1DM (OR = 1.277 (1.024-1.531), P = 0.016). In the reverse MR analysis, it was observed that GDM (OR = 1.112 (1.023-1.201, P = 0.009) is associated with an elevated risk of bacterial pneumonia. However, no significant association was observed bacterial pneumonia with T1DM and other types of diabetes (P > 0.05).

CONCLUSION

This study utilizing MR methodology yields robust evidence supporting a bidirectional causal association between bacterial pneumonia and GDM. Furthermore, our findings suggest a plausible causal link between bacterial pneumonia and T1DM.

Research on the causal relationship between fine particulate matter and type 2 diabetes mellitus: A two-sample multivariable mendelian randomization study

BACKGROUND AND AIMS

Previous research has suggested a correlation between fine particulate matter (PM2.5) and type 2 diabetes mellitus (T2DM). However, the causality was vulnerable to confounding variables.

METHODS AND RESULTS

A two-sample multivariable mendelian randomization study was designed to examine the causal connection between PM2.5 and T2DM. PM2.5 trait was investigated as exposure while T2DM-related traits as outcomes. The summary data were obtained from the Finngen database and the open genome-wide association study database. The mendelian randomization estimates were obtained using the inverse-variance weighted approach, and multiple sensitivity analyses were conducted. There were potential causal relationships between PM2.5 and T2DM (OR = 2.418; P = 0.019), PM2.5 and glycated hemoglobin (HbA1c) (OR = 1.590; P = 0.041), and PM2.5 and insulin metabolism. PM2.5 was found to have no causal effect on fasting glucose and insulin, 2-h glucose, and insulin-like growth factor binding protein-1 (P > 0.05), while had a potential protective effect against some diabetes complications.

CONCLUSIONS

Our findings indicated potential causal relationships among PM2.5 and T2DM, especially the causal relationship between PM2.5 and long-term glucose levels.

Exploring the relationship between air pollution, non-alcoholic fatty liver disease, and liver function indicators: a two-sample Mendelian randomization analysis study

Background and aims

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder worldwide, with an increasing incidence in recent years. While previous studies have suggested an association between the air pollutant PM2.5 and NAFLD, there is still considerable debate regarding the existence of a clear causal relationship between air pollution and NAFLD. This study aims to employ Mendelian randomization methods to evaluate the causal relationship between major air pollutants and NAFLD.

Method

We conducted Mendelian randomization analyses on a large-scale publicly available genome-wide association study (GWAS) dataset of European populations to dissect the association between air pollutants, NAFLD, and liver function indicators. We used five different analysis methods, including Inverse-variance weighted (IVW), Weighted median, MR-Egger, Simple mode, and Weighted mode, to analyze the data. We also tested for pleiotropy, heterogeneity, and sensitivity of the results.

Results

This study utilized four common exposures related to air pollution and four outcomes related to NAFLD. The results regarding the association between air pollutants and NAFLD (PM2.5: P=0.808, 95% CI=0.37-3.56; PM10: P=0.238, 95% CI=0.33-1.31; nitrogen dioxide: P=0.629, 95% CI=0.40-4.61; nitrogen oxides: P=0.123, 95% CI=0.13-1.28) indicated no statistically significant correlation between them. However, notably, there was a causal relationship between PM10 and serum albumin (ALB) levels (P=0.019, 95% CI=1.02-1.27).

Conclusion

This MR study found no evidence of a causal relationship between air pollution and NAFLD in European populations. However, a statistically significant association was observed between PM10 and ALB levels, suggesting that the air pollutant PM10 may impact the liver's ability to synthesize proteins.

Reply to 'The causal relationship between long-term exposure to ambient fine particulate matter and cognitive performance: Insights from Mendelian randomization'

Li et al. [1] have commented on our recent paper investigating the association between exposure to fine particulate matter (PM2.5) constituents and the risk of cognitive impairment [2]. They provided a Mendelian randomization (MR) analysis using large-scale genome-wide association study (GWAS) datasets from the European population, confirming a causal relationship between PM2.5 exposure and cognitive performance. In our reply, we employed three causal inference models, including a generalized propensity score (GPS) adjusted Cox model, an inverse-probability weights (IPW) weighted Cox model, and a trimmed IPW-weighted Cox model, to confirm the relationship of PM2.5 and cognitive impairment in our study cohort.

The impact of PM2.5 on lung function and chronic respiratory diseases: insights from genetic evidence

BACKGROUND

PM2.5 has been associated with various adverse health effects, particularly affecting lung function and chronic respiratory diseases. However, the genetic causality relationship between PM2.5 exposure and lung function as well as chronic respiratory diseases remains poorly understood.

METHOD

We conducted a two-sample Mendelian randomization analysis to investigate the causal impact of PM2.5 on lung function and chronic respiratory diseases. Instrumental variables were carefully selected, with significance thresholds (P < 5 × 10- 8), and linkage disequilibrium with an r2 value below 0.001. Additionally, SNPs with an F-statistic exceeding 10 were included to mitigate potential bias stemming from weak instrumental variables. The primary analytical approach employed the Inverse Variance Weighted method, supplemented by the Weighted Median, MR-Egger, Simple Model, and Weighted Model. Furthermore, pleiotropy and heterogeneity were evaluated through the MR-Egger intercept test and Cochrane's Q test, with a sensitivity analysis conducted using the leave-one-out method.

RESULTS

Eight SNPs significantly associated with PM2.5 exposure were identified as Instrumental variables. Mendelian randomization analysis revealed a significant causal association between PM2.5 exposure and lung function (FEV), with an OR of 0.7284 (95% CI: 0.5799-0.9150). Similarly, PM2.5 exposure demonstrated a substantial causal effect on asthma, with an OR of 1.5280 (95% CI: 1.0470-2.2299). However, no causal association was observed between PM2.5 exposure and chronic obstructive pulmonary disease, with an OR of 1.5176 (95% CI: 0.8294-2.7768).

CONCLUSION

These findings emphasize the necessity for continued research efforts in environmental health to develop effective strategies for the prevention and management of chronic respiratory diseases.

Exploring potential causal links between air pollutants and congenital malformations: A two-sample Mendelian Randomization study

Observational studies have suggested an association between air pollutants and congenital malformations; however, conclusions are inconsistent and the causal associations have not been elucidated. In this study, based on publicly available genetic data, a two-sample Mendelian randomization (MR) was applied to explore the associations between particulate matter 2.5 (PM2.5), NOX, NO2 levels and 11 congenital malformations. Inverse variance weighted (IVW), MR-Egger and weighted median were used as analytical methods, with IVW being the main method. A series of sensitivity analyses were used to verify the robustness of the results. For significant associations, multivariable MR (MVMR) was utilized to explore possible mediating effects. The IVW results showed that PM2.5 was associated with congenital malformations of digestive system (OR = 7.72, 95 %CI = 2.33-25.54, P = 8.11E-4) and multiple systems (OR = 8.63, 95 %CI = 1.02-73.43, P = 0.048) risks; NOX was associated with circulatory system (OR = 4.65, 95 %CI = 1.15-18.86, P = 0.031) and cardiac septal defects (OR = 14.09, 95 %CI = 1.62-122.59, P = 0.017) risks; NO2 was correlated with digestive system (OR = 27.12, 95 %CI = 1.81-407.07, P = 0.017) and cardiac septal defects (OR = 22.57, 95 %CI = 2.50-203.45, P = 0.005) risks. Further MVMR analyses suggest that there may be interactions in the effects of these air pollutants on congenital malformations. In conclusion, this study demonstrated a causal association between air pollution and congenital malformations from a genetic perspective.

The impact of PM2.5 and its constituents on gestational diabetes mellitus: a retrospective cohort study

BACKGROUND

There is increasing evidence that exposure to PM2.5 and its constituents is associated with an increased risk of gestational diabetes mellitus (GDM), but studies on the relationship between exposure to PM2.5 constituents and the risk of GDM are still limited.

METHODS

A total of 17,855 pregnant women in Guangzhou were recruited for this retrospective cohort study, and the time-varying average concentration method was used to estimate individual exposure to PM2.5 and its constituents during pregnancy. Logistic regression was used to assess the relationship between exposure to PM2.5 and its constituents and the risk of GDM, and the expected inflection point between exposure to PM2.5 and its constituents and the risk of GDM was estimated using logistic regression combined with restricted cubic spline curves. Stratified analyses and interaction tests were performed.

RESULTS

After adjustment for confounders, exposure to PM2.5 and its constituents (NO3-, NH4+, and OM) was positively associated with the risk of GDM during pregnancy, especially when exposure to NO3- and NH4+ occurred in the first to second trimester, with each interquartile range increase the risk of GDM by 20.2% (95% CI: 1.118-1.293) and 18.2% (95% CI. 1.107-1.263), respectively. The lowest inflection points between PM2.5, SO42-, NO3-, NH4+, OM, and BC concentrations and GDM risk throughout the gestation period were 18.96, 5.80, 3.22, 2.67, 4.77 and 0.97 µg/m3, respectively. In the first trimester, an age interaction effect between exposure to SO42-, OM, and BC and the risk of GDM was observed.

CONCLUSIONS

This study demonstrates a positive association between exposure to PM2.5 and its constituents and the risk of GDM. Specifically, exposure to NO3-, NH4+, and OM was particularly associated with an increased risk of GDM. The present study contributes to a better understanding of the effects of exposure to PM2.5 and its constituents on the risk of GDM.

Association between PM2.5 constituents and cardiometabolic risk factors: Exploring individual and combined effects, and mediating inflammation

BACKGROUND

The individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear.

METHOD

A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively.

RESULT

PM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP.

CONCLUSION

Our study suggested associations of individual and combined PM2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.

Causal relationship and shared genes between air pollutants and amyotrophic lateral sclerosis: A large-scale genetic analysis

OBJECTIVE

Air pollutants have been reported to have a potential relationship with amyotrophic lateral sclerosis (ALS). The causality and underlying mechanism remained unknown despite several existing observational studies. We aimed to investigate the potential causality between air pollutants (PM2.5, NOX, and NO2) and the risk of ALS and elucidate the underlying mechanisms associated with this relationship.

METHODS

The data utilized in our study were obtained from publicly available genome-wide association study data sets, in which single nucleotide polymorphisms (SNPs) were employed as the instrumental variantswith three principles. Two-sample Mendelian randomization and transcriptome-wide association (TWAS) analyses were conducted to evaluate the effects of air pollutants on ALS and identify genes associated with both pollutants and ALS, followed by regulatory network prediction.

RESULTS

We observed that exposure to a high level of PM2.5 (OR: 2.40 [95% CI: 1.26-4.57], p = 7.46E-3) and NOx (OR: 2.35 [95% CI: 1.32-4.17], p = 3.65E-3) genetically increased the incidence of ALS in MR analysis, while the effects of NO2 showed a similar trend but without sufficient significance. In the TWAS analysis, TMEM175 and USP35 turned out to be the genes shared between PM2.5 and ALS in the same direction.

CONCLUSION

Higher exposure to PM2.5 and NOX might causally increase the risk of ALS. Avoiding exposure to air pollutants and air cleaning might be necessary for ALS prevention.

The causal effect of exposure to air pollution on risk of adverse pregnancy outcomes: A two-sample Mendelian randomisation study

BACKGROUND

Epidemiological studies have examined the relation between air pollution (NOx, NO2, PM2.5, PM2.5-10, and PM10) and adverse pregnancy outcomes (APOs). There's increasing evidence that air pollution increases the risk of APOs. However, the results of these studies are controversial, and the causal relation remains uncertain. We aimed to assess whether a genetic causal link exists between air pollution and APOs and the potential effects of this relation.

METHODS

A novel two-sample Mendelian randomisation (MR) study used pooled data from a large-scale complete genome correlation study. The primary analysis method was inverse variance weighting (IVW), which explored the expose-outcome relationship for assessing single nucleotide polymorphisms (SNPs) associated with air pollution. Further sensitivity analysis, including MR-PRESSO, MR-Egger regression, and leave-one analysis, was used to test the consistency of the results.

RESULTS

There was a significant correlation between air pollution-related SNPs and APOs. A robust causal link was found between genetic susceptibility to air pollution and APOs.

CONCLUSIONS

Our MR analysis reveals a genetic causal relation between air pollution and APOs, which may help provide new insights into further mechanisms and clinical studies in air pollution-mediated APOs.

Causal relationship between air pollution, lung function, gastroesophageal reflux disease, and non-alcoholic fatty liver disease: univariate and multivariate Mendelian randomization study

Background

The association between air pollution, lung function, gastroesophageal reflux disease, and Non-alcoholic fatty liver disease (NAFLD) remains inconclusive. Previous studies were not convincing due to confounding factors and reverse causality. We aim to investigate the causal relationship between air pollution, lung function, gastroesophageal reflux disease, and NAFLD using Mendelian randomization analysis.

Methods

In this study, univariate Mendelian randomization analysis was conducted first. Subsequently, Steiger testing was performed to exclude the possibility of reverse association. Finally, significant risk factors identified from the univariate Mendelian analysis, as well as important factors affecting NAFLD from previous observational studies (type 2 diabetes and body mass index), were included in the multivariable Mendelian randomization analysis.

Results

The results of the univariable Mendelian randomization analysis showed a positive correlation between particulate matter 2.5, gastroesophageal reflux disease, and NAFLD. There was a negative correlation between forced expiratory volume in 1 s, forced vital capacity, and NAFLD. The multivariable Mendelian randomization analysis indicated a direct causal relationship between gastroesophageal reflux disease (OR = 1.537, p = 0.011), type 2 diabetes (OR = 1.261, p < 0.001), and NAFLD.

Conclusion

This Mendelian randomization study confirmed the causal relationships between air pollution, lung function, gastroesophageal reflux, and NAFLD. Furthermore, gastroesophageal reflux and type 2 diabetes were identified as independent risk factors for NAFLD, having a direct causal connection with the occurrence of NAFLD.

Causal role of immune cell phenotypes in idiopathic sudden sensorineural hearing loss: a bi-directional Mendelian randomization study

Background

A growing body of evidence suggests that immunological processes have a significant role in developing idiopathic sudden sensorineural hearing loss (SSHL). However, few studies have examined the association between immune cell phenotype and SSHL using Mendelian Randomization (MR).

Methods

The online genome-wide association studies (GWAS) database was used to compile data from GWAS covering 731 immunophenotypes and SSHL. Inverse variance weighted (IVW) analysis was primarily used for MR study, and single nucleotide polymorphisms (SNPs) associated with immunophenotypes served as dependent variables. A sensitivity study and the false discovery rate (FDR) correction were used to examine the MR hypothesis. In addition, the possibility of reverse causality between immunophenotype and SSHL was validated by reverse MR. Reverse MR was analyzed in a manner consistent with forward MR.

Results

After FDR correction and sensitivity analysis, we screened 7 immunophenotypes, including IgD+ CD38dim %lymphocyte (95% CI: 1.0019, 1.0742, p = 3.87 × 10-2, FDR = 1.15 × 10-2); Unsw mem AC (95% CI: 1.004, 1.2522, p = 4.23 × 10-2, FDR = 2.25 × 10-2); CD86+ myeloid DC AC (95% CI: 1.0083, 1.1147, p = 2.24 × 10-2, FDR = 4.27 × 10-2); CD33dim HLA DR- AC (95% CI: 1.0046, 1.0583, p = 2.12 × 10-2, FDR = 4.69 × 10-2); SSC-A on CD8br (95% CI: 1.0028, 1.1461, p = 4.12 × 10-2, FDR = 4.71 × 10-2); CD45RA- CD4+ %T cell (95% CI: 1.0036, 1.0503, p = 2.32 × 10-2, FDR = 4.82 × 10-2); DP (CD4+CD8+) AC (95% CI: 1.011, 1.2091, p = 2.78 × 10-2, FDR = 4.97 × 10-2). There was a strong causal relationship with SSHL onset, and the reliability of the results was verified. Furthermore, the immunological cell profile and SSHL did not appear to be closely associated, as shown by reverse MR analysis.

Conclusion

Our study provides more support for the current hypothesis that immunophenotypes and the pathophysiology of SSHL are closely associated. Further validation is needed to assess the role of these immunophenotypes in SSHL.

Particulate matter 2.5 causally increased genetic risk of autism spectrum disorder

BACKGROUND

Growing evidence suggested that particulate matter (PM) exhibit an increased risk of autism spectrum disorder (ASD). However, the causal association between PM and ASD risk remains unclear.

METHODS

We performed two-sample Mendelian randomization (MR) analyses, using instrumental variables (IVs) sourced from the largest genome-wide association studies (GWAS) databases. We employed three MR methods: inverse-variance weighted (IVW), weighted median (WM), and MR-Egger, with IVW method serving as our primary MR method. Sensitivity analyses were performed to ensure the stability of these findings.

RESULTS

The MR results suggested that PM2.5 increased the genetic risk of ASD (β = 2.41, OR = 11.13, 95% CI: 2.54-48.76, P < 0.01), and similar result was found for PM2.5 absorbance (β = 1.54, OR = 4.67, 95% CI: 1.21-18.01, P = 0.03). However, no such association was found in PM10 (β = 0.27, OR = 1.30, 95% CI: 0.72-2.36, P = 0.38). After adjusting for the false discovery rate (FDR) correction, our MR results remain consistent. Sensitivity analyses did not find significant heterogeneity or horizontal pleiotropy.

CONCLUSIONS

Our findings indicate that PM2.5 is a potential risk factor for ASD. Effective strategies to mitigate air pollutants might lead to a reduced incidence of ASD.

Causal relationship between particulate matter 2.5 and infectious diseases: A two-sample Mendelian randomization study

Background

Previous 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).

Methods

Inverse 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.

Results

PM2.5 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.

Conclusions

Overall, 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.

A two-sample Mendelian randomization analysis: causal association between chemokines and pan-carcinoma

Objective: According to the 2020 data from the World Health Organization (WHO), cancers stand as one of the foremost contributors to global mortality. Revealing novel cancer risk factors and protective factors is of paramount importance in the prevention of disease occurrence. Studies on the relationship between chemokines and cancer are ongoing; however, due to the coordination of multiple potential mechanisms, the specific causal association remains unclear. Methods: We performed a bidirectional Mendelian randomization analysis to explore the causal association between serum chemokines and pan-carcinoma. All data is from the GWAS catalog and IEU Open GWAS database. The inverse-variance weighted (IVW) method is primarily employed for assessing the statistical significance of the findings. In addition, the significance threshold after the multiple hypothesis test (Bonferroni) was 0.0013, and the evidence of a potential association was considered if the p-value < 0.05, but remained greater than Bonferroni's threshold. Results: The results indicate that CCL1 (odds ratio, OR = 1.18), CCL2 (OR = 1.04), CCL8 (OR = 1.36), CCL14 (Colorectal, OR = 1.08, Small intestine, OR = 0.77, Lung, OR = 1.11), CCL15 (OR = 0.85), CCL18 (Breast, OR = 0.95, Prostate, OR = 0.96), CCL19 (Lung, OR = 0.66, Prostate, OR = 0.92), CCL20 (Lung, OR = 0.53, Thyroid, OR = 0.76), CCL21 (OR = 0.62), CCL22 (OR = 2.05), CCL23 (OR = 1.31), CCL24 (OR = 1.06), CCL27 (OR = 1.49), CCL28 (OR = 0.74), CXCL5 (OR = 0.95), CXCL9 (OR = 3.60), CXCL12 (Breast, OR = 0.87, Small intestine, OR = 0.58), CXCL13 (Breast, OR = 0.93, Lung, OR = 1.29), CXCL14 (Colon, OR = 1.40) and CXCL17 (OR = 1.07) are potential risk factors for cancers. In addition, there was a reverse causal association between CCL1 (OR = 0.94) and CCL18 (OR = 0.94) and breast cancer. Sensitivity analysis results were similar. The results of the other four MR Methods were consistent with the main results, and the leave-one-out method showed that the results were not driven by a Single nucleotide polymorphism (SNP). Moreover, there was no heterogeneity and pleiotropy in our analysis. Conclusion: Based on the two-sample MR Analysis method, we found that chemokines might be upstream factors of cancer pathogenesis. These results might provide new insights into the future use of chemokines as potential targets for cancer prevention and treatment. Our results also provide important clues for tumor prevention, and changes of serum chemokine concentration may be recognized as one of the features of precancerous lesions in future clinical trials.

Causal relationship between gut microbiota and myasthenia gravis: a bidirectional mendelian randomization study

BACKGROUND

Observational studies have demonstrated an association between gut microbiota and myasthenia gravis; however, the causal relationship between the two still lacks clarity. Our goals are to ascertain the existence of a bidirectional causal relationship between gut microbiota composition and myasthenia gravis, and to investigate how gut microbiota plays a role in reducing the risk of myasthenia gravis.

METHODS

We acquired gut microbiota data at the phylum, class, order, family, and genus levels from the MiBioGen consortium (N = 18,340) and myasthenia gravis data from the FinnGen Research Project (426 cases and 373,848 controls). In the two-sample Mendelian randomization analysis, we assessed the causal relationship between the gut microbiota and myasthenia gravis. We also conducted bidirectional MR analysis to determine the direction of causality. The inverse variance weighted, mendelian randomization-Egger, weighted median, simple mode, and weighted mode were used to test the causal relationship between the gut microbiota and severe myasthenia gravis. We used MR-Egger intercept and Cochran's Q test to assess for pleiotropy and heterogeneity, respectively. Furthermore, we utilized the MR-PRESSO method to evaluate horizontal pleiotropy and detect outliers.

RESULTS

In the forward analysis, the inverse-variance weighted method revealed that there is a positive correlation between the genus Lachnoclostridium (OR = 2.431,95%CI 1.047-5.647, p = 0.039) and the risk of myasthenia gravis. Additionally, the family Clostridiaceae1 (OR = 0.424,95%CI 0.202-0.889, p = 0.023), family Defluviitaleaceae (OR = 0.537,95%CI  0.290-0.995, p = 0.048), family Enterobacteriaceae (OR = 0.341,95%CI  0.135-0.865, p = 0.023), and an unknown genus (OR = 0.407,95%CI  0.209-0.793, p = 0.008) all demonstrated negative correlation with the risk of developing myasthenia gravis. Futhermore, reversed Mendelian randomization analysis proved a negative correlation between the risk of myasthenia gravis and genus Barnesiella (OR = 0.945,95%CI  0.906-0.985, p = 0.008).

CONCLUSION

Our research yielded evidence of a causality connection in both directions between gut microbiota and myasthenia gravis. We identified specific types of microbes associated with myasthenia gravis, which offers a fresh window into the pathogenesis of this disease and the possibility of developing treatment strategies. Nonetheless, more studies, both basic and clinical, are necessary to elucidate the precise role and therapeutic potential of the gut microbiota in the pathogenesis of myasthenia gravis.

Causality of particulate matter on cardiovascular diseases and cardiovascular biomarkers

Background

Previous observational studies have shown that the prevalence of cardiovascular diseases (CVDs) is related to particulate matter (PM). However, given the methodological limitations of conventional observational research, it is difficult to identify causality conclusively. To explore the causality of PM on CVDs and cardiovascular biomarkers, we conducted a Mendelian randomization (MR) analysis.

Method

In this study, we obtained summary-level data for CVDs and cardiovascular biomarkers including atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), ischemic stroke (IS), stroke subtypes, body mass index (BMI), lipid traits, fasting glucose, fasting insulin, and blood pressure from several large genome-wide association studies (GWASs). Then we used two-sample MR to assess the causality of PM on CVDs and cardiovascular biomarkers, 16 single nucleotide polymorphisms (SNPs) for PM2.5 and 6 SNPs for PM10 were obtained from UK Biobank participants. Inverse variance weighting (IVW) analyses under the fixed effects model were used as the main analytical method to calculate MR Estimates, followed by multiple sensitivity analyses to confirm the robustness of the results.

Results

Our study revealed increases in PM2.5 concentration were significantly related to a higher risk of MI (odds ratio (OR), 2.578; 95% confidence interval (CI), 1.611-4.127; p = 7.920 × 10-5). Suggestive evidence was found between PM10 concentration and HF (OR, 2.015; 95% CI, 1.082-3.753; p = 0.027) and IS (OR, 2.279; 95% CI,1.099-4.723; p = 0.027). There was no evidence for an effect of PM concentration on other CVDs. Furthermore, PM2.5 concentration increases were significantly associated with increases in triglyceride (TG) (OR, 1.426; 95% CI, 1.133-1.795; p = 2.469 × 10-3) and decreases in high-density lipoprotein cholesterol (HDL-C) (OR, 0.779; 95% CI, 0.615-0.986; p = 0.038). The PM10 concentration increases were also closely related to the decreases in HDL-C (OR, 0.563; 95% CI, 0.366-0.865; p = 8.756 × 10-3). We observed no causal effect of PM on other cardiovascular biomarkers.

Conclusion

At the genetic level, our study suggested the causality of PM2.5 on MI, TG, as well HDL-C, and revealed the causality of PM10 on HF, IS, and HDL-C. Our findings indicated the need for continued improvements in air pollution abatement for CVDs prevention.