
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312374
MD-D-24-06267
00075
10.1097/MD.0000000000039708
3
4700
Research Article
Observational Study
Mendelian study on air pollution and membranous nephropathy outcomes associations
Zhu Xiaoxiao MMed 602136739@qq.com
a
Zhou Hanjing MMed zhjjhzxyy@163.com
b
Xu Wanxian MD cd*
a Traditional Chinese Medicine (Zhong Jing) College, Henan University of Chinese Medicine, Zhengzhou, China
b Department of Nephrology, Jinhua Hospital of Zhejiang University, Jinhua, China
c Department of Breast and Thyroid Surgery, First People’s Hospital of Kunming City & Calmette Affiliated Hospital of Kunming Medical University, Kunming, China
d Kunming Medical University, Kunming, China.
* Correspondence: Wanxian Xu, Department of Breast and Thyroid Surgery, First People’s Hospital of Kunming City & Calmette Affiliated Hospital of Kunming Medical University, Panglong District, Kunming, Yunnan 650032, China (e-mail: 20221572@kmmu.edu.cn).
20 9 2024
20 9 2024
103 38 e3970805 6 2024
05 7 2024
23 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Membranous nephropathy (MN) is an autoimmune disease of the kidney glomerulus, which mainly leads to nephrotic syndrome. This study investigates the associations between air pollution and MN risk and from an epigenomic perspective. In this study, we examine the associations between genetically predicted deoxyribonucleic acid methylation related to air pollution and MN risk. The data of air pollution included particulate matter (PM) with a diameter of 2.5 µm or less (PM2.5), PM with a diameter between 2.5 and 10 µm (PM2.5–10), PM with a diameter of 10 µm or less (PM10), nitrogen dioxide, and nitrogen oxides. Inverse variance weighted method was used as the main analysis method, and weighted median model and Mendelian randomization-Egger methods were selected for quality control. To assess the reliability of the results of the analyses, heterogeneity test, horizontal pleiotropy test, and the leave-one-out method were applied. There was a causal relationship between nitrogen oxides and MN risk (P = .010). Other types of air pollution were found no statistical association with MN disease (PM2.5: P = .378; PM2.5–10: P = .111; PM10: P = .035; nitrogen dioxide: P = .094). There was no heterogeneity or pleiotropy in the results. Our study suggests the association between nitrogen oxides and membrane nephropathy (MN) risk from the genetic perspective. This provides a theoretical basis for the prevention of MN disease.

air pollution
membranous nephropathy
Mendelian randomization analysis
multiple corrections
reverse Mendelian randomization analysis
OPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

Globally, air pollution, which consists of a complex mixture of various gases such as nitrogen oxides and particulate matter (PM), is the fifth leading risk factor for death.[1,2] Air pollution is responsible for 1 in 9 deaths worldwide from noncommunicable diseases such as cardiovascular disease, respiratory illnesses, and cancer.[3] Previous epidemiological studies have reported an association between exposure to ambient air pollution and diseases of the lungs and cardiovascular system.[4,5] In addition, air pollution has emerged as a major contributor to the global stroke burden, with reducing exposure identified as a main priority to alleviate this issue. Substantial evidence links certain air pollutants, such as PM with a diameter of 2.5 µm or less (PM2.5), nitrogen dioxide (NO2), and nitrogen oxides, to cardiovascular disease. Exposure to PM with a diameter of 10 µm or less (PM10) has been associated with cardiovascular and pulmonary diseases, corneal diseases, and cancer.[6–9]

Membranous nephropathy (MN) is one of the most prevalent causes of nephrotic syndrome in adults. It is an autoimmune disorder that affects the kidney glomerulus.[10] Its prevalence has changed over the years, making it the most common pathological finding in China between 2015 and 2019.[11] It accounts for 15% of kidney biopsies performed in Singapore between 2008 and 2018.[12] MN is an immunocomplex disease, because pathogenesis begins with the binding of circulating antibodies to antigens present on podocytes. In the secondary forms, the initiating trigger could be the deposition of immune complexes in the subepithelial space.[13]

Mendelian randomization (MR) is a statistical approach that uses genetic variation to look for causal relationships between exposures and outcomes.[14,15] The choice of the genetic instrumental variable is essential to a successful MR study. There is a relatively small body of literature showing the health risks associated with membraneous nephropathy. The relationship between air pollution and MN remains largely uncertain. Therefore, in this study, we used data from genome-wide association study (GWAS)-related public databases and MR analysis methods to explore the causal relationship between air pollution and MN, which was important for further revealing the pathogenesis of membraneous nephropathy.

2. Methods and materials

2.1. Data source

We conducted a bidirectional MR analysis to investigate the causal association between air pollution and MN. First, we obtained data on 5 air pollution indices from the open GWAS database (IEU OpenGWAS project [mrcieu.ac.uk]), all of which belong to the UK Biobank and focus on European populations (Table 1). MN data were obtained from the GWAS Catalog, also focusing on European populations, with 2150 cases and 5829 controls (https://www.ebi.ac.uk/gwas/). Next, we used the “TwoSampleMR” R package for exposure factors reading and instrumental variables (IVs) screening. The selection criteria for single nucleotide polymorphisms (SNPs) as genetic IVs included GWA (P < 5 × 10−6), independent inheritance (R2 = 0.001), and no linkage disequilibrium (kb = 10,000). The F value was calculated by F = (beta/se),[2] and only SNPs with F > 10 were retained (Supplemental Digital Content S1, http://links.lww.com/MD/N569). These criteria ensured the validity and reliability of the selected genetic variants in our MR analysis.[16] It is worth noting that all participants behind the data in the study obtained approval from relevant institutions and associations and signed informed consent forms.

Table 1 Air pollution in the details.

GWAS ID	Trait	Sample size	SNPs	Consortium	Population	
ukb-b-12417	Nitrogen oxides	456,380	9851867	MRC-IEU	European	
ukb-b-2618	Nitrogen dioxide	456,180	9851867	MRC-IEU	European	
ukb-b-10817	PM2.5 µm	423,796	9851867	MRC-IEU	European	
ukb-b-12963	PM2.5–10 µm	423,796	9851867	MRC-IEU	European	
ukb-b-589	PM10 µm	455,314	9851867	MRC-IEU	European	
GWAS = genome-wide association study.

2.2. MR analysis

We employed 5 methods to perform forward and reverse MR analyses. First, MR-Egger regression was used to test whether a causal relationship existed between variables. This method assumes that pleiotropic associations are independent, helping to identify any invalid results. If all genetic variation is nullified, it can provide consistent estimates of causal effects.[17] However, MR-Egger estimates may be inaccurate and significantly affected by peripheral genetic variants. Second, the weighted median method can provide accurate estimates of causal effects if at least 50% of the analysis weights come from valid IVs.[18] Third, the inverse variance weighted (IVW) method was used to calculate the Wald ratio for each SNP to estimate the relationship between exposure factors and outcomes.[19] Although the simple mode method may not be as robust as IVW, it still offers robustness against pleiotropy. Selecting the bandwidth for the weighted mode can be challenging. Among these methods, the IVW method was the primary method of analysis.[20] All analyses were conducted using the “TwoSampleMR” R package. In this study, we also created a flow diagram for the MR study (Fig. 1).

Figure 1. Flow diagram for MR study. MR = Mendelian randomization.

Before conducting the MR analysis, we first performed a horizontal pleiotropy test on the data. For SNPs with horizontal pleiotropy, we used MR-PRESSO to remove outliers, while SNPs without horizontal pleiotropy required no special treatment. During the MR analysis, we performed a heterogeneity test on the processed data. For SNPs with heterogeneity, the IVW method employed a random effects model, whereas for SNPs without heterogeneity, the IVW method used a fixed effects model. Additionally, we calculated the odds ratio (OR).

2.3. Sensitivity analysis

In this study, heterogeneity was assessed using IVW and MR-Egger regression methods, and quantified using the Cochrane Q test.[21] The Q-value was <0.05, indicating that there was heterogeneity among SNPs. On the contrary, Q-value was >0.05, suggesting that the analysis was valid. In MR-Egger regression, the intercept term is an effective indicator of whether directional horizontal pleiotropy affects the result of the MR analysis.[22] The P value >.05 indicated that no significant horizontal pleiotropy was found, which suggested that the IVW method could achieve unbiased causal estimation and the analysis results of IVW were reliable. To confirm the stability of the MR analysis, we carried out a leave-one-out analysis to find out if a single SNP could change the overall effects of all the SNPs in the IVW analysis.[23,24]

3. Results

3.1. MR analysis

In the sensitivity analysis, the analysis of horizontal pleiotropy resulted in a P value of .864 (Supplemental Digital Content S2, http://links.lww.com/MD/N569), indicating that there were no multiple horizontal effects. Besides, the result of Cochrane Q test was 0.09 (Supplemental Digital Content S3, http://links.lww.com/MD/N569), indicating there was no heterogeneity among SNPs. And we concluded that there was a causal relationship between nitrogen oxides and MN. To assess the causal effects of air pollution (including nitrogen dioxide, nitrogen oxides, PM2.5, PM5–10, and PM10) on MN, we conducted the forward MR analysis in Europe. In the preliminary IVW analysis, we selected closely related SNPs as genetic tools for MN. Although the p was significant before multiple corrections (Supplemental Digital Content S4, http://links.lww.com/MD/N569), there is no relationship between PM10 and MN. After multiple corrections, we observed a positively correlated link between nitrogen oxides and MN (OR = 54.99, 95% confidence interval [1.92–1574.79], P = .019) (Supplemental Digital Content S5, http://links.lww.com/MD/N569). Nitrogen oxides might be a risk factor for MN since OR was more than 1 (OR = 54.99). In addition, there is no genetic causal link between other air pollution (Nitrogen dioxide, PM2.5, PM5–10) and MN. The results of the leave-one-out method are shown in Figure 2A. There were no points of serious bias in the map, indicating that the results were reliable. It could be seen from Figure 2A and B, the slope of the line was positive and there was no intercept in the figure, indicating that nitrogen oxides were a risk factor for MN. The forest maps were used to determine the diagnostic efficacy of the predicted exposure factors for each SNP site. The funnel plot in Figure 2C and D illustrated that the IVW-based MR analysis conformed to Mendel’s second law of random grouping (Fig. 2).

Figure 2. MR result of between nitrogen oxides and MN. (A) Leave-one-out analyses for nitrogen oxides on MN to evaluate the effect of each SNP in driving causality. (B) A forest map indicated the MR effect size (gray line segment) for each SNP on outcome. (C) A funnel plot illustrated the IVW-based MR analysis. (D) Scatter plot of the association between nitrogen oxides and MN. The 5 methods applied in the current manuscript were all depicted. The light blue, blue, light green, green, and red represented IVW, MR-Egger, simple mode, weighted median, and weight mode methods, respectively. IVW = inverse variance weighted, MN = membranous nephropathy, MR = Mendelian randomization, SNP = single nucleotide polymorphism.

However, there were 18 SNPs for reverse MR analysis. From the result of IVW (OR = 0.998, P = .242), it was not significant (Supplemental Digital Content S6, http://links.lww.com/MD/N569), which indicated that MN was not causally related to nitrogen oxides.

4. Discussion

Some evidence has shown that MN is an exposure factor for lung cancer, rheumatoid arthritis, and gut microbiota changes, such as increases in Bifidobacterium bifidum.[25] In this study, we are the first to use the MR method to predict the causal association between exposure to air pollution and MN. Our findings indicate that genetically predicted nitrogen oxides could increase the risk of MN. However, other types of air pollution, including NO2, PM2.5, PM5–10, and PM10, did not show a positive association with MN.[26]

Emerging research links exposure to environmental pollutants, such as air pollution, to an increased prevalence and severity of both physical and mental disorders. Exposure to human-made and naturally occurring toxins in the air can lead to their accumulation in organs, contributing to morbidity and mortality. Over the past decade, a growing body of research has suggested a causal relationship between ambient air pollution exposure and adverse cardiovascular health outcomes.[27] Patients with kidney disease may be especially susceptible to the effects of environmental exposures, because they are innate frailty and high comorbidity burden.[28] Patients with kidney disease may be particularly susceptible to the effects of environmental exposures due to their inherent frailty and high comorbidity burden.

Our research results are consistent with previous studies, emphasizing the importance of reducing air pollution for the prevention of kidney disease. This aligns with the broader understanding that environmental health is critical for overall well-being, especially for vulnerable populations such as those with chronic kidney conditions. Thus, implementing stricter air quality regulations and promoting public health initiatives to reduce air pollution could have significant benefits in preventing kidney diseases and improving public health outcomes more generally. Evidence shows that MN is an exposure factor for lung cancer, rheumatoid arthritis, and changes in gut microbiota, such as increases in B bifidum. Our study is the first to use the MR method to predict the causal association between exposure to air pollution and MN. Genetically predicted nitrogen oxides could increase the risk of MN. Other types of air pollution, including NO2, PM2.5, PM5–10, and PM10, did not show a positive association with MN. Emerging research links exposure to environmental pollutants, such as air pollution, to increased prevalence and severity of physical and mental disorders. Exposure to human-made and naturally occurring toxins in the air can lead to their accumulation in organs, contributing to morbidity and mortality. Over the past decade, research has suggested a causal relationship between ambient air pollution exposure and adverse cardiovascular health outcomes. Patients with kidney disease may be particularly susceptible to environmental exposures due to their inherent frailty and high comorbidity burden. Our research highlights the importance of reducing air pollution for the prevention of kidney disease. Stricter air quality regulations and public health initiatives could prevent kidney diseases and improve overall public health outcomes.

These findings lay the groundwork for future research exploring the interplay between MN and exposure to air pollutants, with substantial implications for the prevention and treatment of MN and its associated complications. It is necessary to raise awareness and mobilize policymakers, industry representatives, and other stakeholders to establish air quality standards, implement emissions controls, and promote the use of greener energy. Additionally, more detailed longitudinal studies are needed to establish clear cause-and-effect relationships between specific air pollutants and MN.

5. Conclusions

The study found that nitrogen oxides in the air are a risk factor for MN, increasing the risk of the disease and accelerating its progression.

Acknowledgments

The authors thank the GWAS catalog and the IEU Open GWAS project for providing open summary results data for the analyses.

Author contributions

Conceptualization: Xiaoxiao Zhu, Hanjing Zhou, Wanxian Xu.

Formal analysis: Xiaoxiao Zhu, Hanjing Zhou, Wanxian Xu.

Investigation: Xiaoxiao Zhu, Hanjing Zhou.

Methodology: Xiaoxiao Zhu, Hanjing Zhou, Wanxian Xu.

Project administration: Xiaoxiao Zhu, Wanxian Xu.

Resources: Xiaoxiao Zhu.

Software: Xiaoxiao Zhu.

Validation: Xiaoxiao Zhu, Hanjing Zhou, Wanxian Xu.

Writing—original draft: Xiaoxiao Zhu, Hanjing Zhou.

Visualization: Hanjing Zhou, Wanxian Xu.

Data curation: Wanxian Xu.

Writing—review & editing: Wanxian Xu.

Supplementary Material

Abbreviations:

GWAS genome-wide association study

IVs instrumental variables

IVW inverse variance weighted

MN membranous nephropathy

MR Mendelian randomization

NO2 nitrogen dioxide

OR odds ratio

PM particulate matter

SNPs single nucleotide polymorphisms

The UK Biobank has obtained ethical approval from the North West Multi-centre Research Ethics Committee (MREC), which covers the UK (REC reference: 11/NW/0382). All participants provided informed consent, and their data are anonymized to protect privacy. The use of data for research purposes is governed by strict protocols to ensure that the data is used responsibly and ethically, in line with the consent provided by participants and relevant legal and regulatory requirements.

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Zhu X, Zhou H, Xu W. Mendelian study on air pollution and membranous nephropathy outcomes associations. Medicine 2024;103:38(e39708).
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