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. 2026 Aug 7;105(32):e50160. doi: 10.1097/MD.0000000000050160

Causal association between air pollution and functional outcome after ischemic stroke: Evidence from 2-sample MR study

YunSen Zhang a, XiaoWei Liu b, YuanHong Ge c, Yong Liu d, RongHua Xu c, Xuejun Xu c,*
PMCID: PMC13456901  PMID: 42566633

Abstract

Observational studies have shown that air pollution may affect functional outcome following ischemic stroke. Our study aimed to explore the relationship between genetically predicted specific air pollutants and poststroke functional outcomes using the Mendelian randomization (MR) methodology. The instrumental variables for air pollution (nitrogen oxide [NOx], nitrogen dioxide [NO2], PM2.5, PM2.5-10, and PM10) were derived from whole-genome association study data of individuals of European descent. Summary data for functional outcomes after ischemic stroke were sourced from the Ischemic Stroke Genetics Functional Outcome Network. Primary analysis employed the “IVW” method for 2-sample MR. In sensitivity analysis, Cochran Q test, inverse variance-weighted (IVW) method, and MR-Egger method were used for heterogeneity assessment. In the horizontal pleiotropy analysis, we utilized the MR-Egger regression and MR pleiotropy residual sum and outlier (MR-PRESSO) test. Finally, the robustness of the results is ensured. Gene prediction suggested no association between several air pollutants and functional outcome after ischemic stroke (PM2.5 [odds ratio (OR) 0.87; 95% confidence interval (CI) 0.26–2.95; P = .825], NOx [OR, 0.77; 95% CI: 0.27–2.21; P = .629], NO2 [OR 1.73; 95% CI: 0.61–4.90; P = .304], PM2.5-10 [OR 2.48; 95% CI: 0.28–22.12; P = .416], and PM10 [OR 2.54; 95% CI: 0.41–15.87; P = .319]). The supplementary MR methods confirmed similar results. Further analysis, not adjusted for stroke severity, supports these findings. Air pollutants (NOx, NO2, PM2.5, PM2.5-10 and PM10) may not have an impact on functional outcome after ischemic stroke.

Keywords: air pollution, causal relationship, functional outcome, ischemic stroke, Mendelian randomization study

1. Introduction

With the progression of industrialization, air pollution has evolved into a significant adverse determinant impacting global public health in contemporary times, imposing a substantial burden on medical and healthcare infrastructure.[1] Geographically, the prevalence of air pollution and its associated detrimental effects are more pronounced in developing regions.[2] The deleterious impact of air pollution on human health permeates across various physiological systems, prominently affecting vital bodily systems, such as the respiratory, circulatory, and nervous systems.[3,4] Excessive levels of PM2.5, PM10, nitrogen dioxide (NO2), sulfur dioxide, ozone, and carbon monoxide in the atmosphere constitute the principal constituents posing threats to health, each inflicting distinctive harm on the human body through diverse pathways.[5]

Stroke is the second leading cause of disability and mortality globally, with low- and middle-income countries having the highest disease burdens. Approximately 87% of stroke cases manifest as ischemic strokes.[6] Escalating levels of air pollution further exacerbate the risk of stroke.[7] However, the functional outcomes following ischemic stroke are often suboptimal, ranging from severe disability to death, exhibiting diverse outcomes.[8] Current research indicates numerous etiological factors contributing to ischemic stroke, with common culprits including atherosclerosis, thromboembolism, and hypertension, among others.[9]

In recent years, the relationship between air pollution and the occurrence of ischemic stroke has been extensively studied in numerous cross sectional and cohort studies conducted.[10] Prolonged exposure to polluted air has been shown to adversely affects neurodegenerative diseases through systemic inflammation, induction of oxidative stress, and direct brain expansion.[11] However, only a limited number of cohort studies have found evidence that air pollution affects the prognosis of functional outcome following ischemic stroke.[12]

To further investigate this relationship, we employed the Mendelian randomization (MR) study methodology. MR is a robust instrumental variable analysis method that utilizes genetic variants associated with the exposure of interest as an instrument to estimate the causal relationships between exposure and disease.[13] By leveraging the inherent random assortment of genetic variants during meiosis, these variants are randomly distributed within the population.[14] As these variants are passed on to subsequent generations through the formation of gametes, it ensures that these genetic variations exist before the onset of the disease, thus eliminating the intervention of confounding factors and ensuring reverse causation.[15] This provides a valuable tool for simulating randomized controlled trials and effectively overcoming the inherent limitations of traditional observational studies.

The MR method has previously been used to investigate the relationship between genetic predisposition to air pollution and the risk of ischemic stroke, but not its association with functional outcomes following ischemic stroke. In this study, we employed a 2-sample MR analysis to investigate the causal relationship between 4 air pollutants (PM2.5, PM10, nitrogen oxide [NOx], and NO2) and functional outcomes following ischemic stroke.

2. Materials and methods

2.1. Study design

Our study was based on an MR design and strictly adhered to the STROBE-MR guidelines.[16] We employed 2-sample MR analysis to determine the causal relationship between air pollution and functional outcome after ischemic stroke. During MR analysis, it is crucial to satisfy 3 key assumptions to ensure the scientific rigor and accuracy of the study. To this end, we constructed an MR flowchart (Fig. 1). First, instrumental variables (IVs) are single-nucleotide polymorphisms (SNPs) closely associated with air pollution (relevance assumption). Second, IVs do not influence outcomes through confounding factors (independence assumption). Lastly, IVs affect functional outcome after ischemic stroke solely through air pollution exposure and not through other pathways (exclusion-restriction assumption).[15]

Figure 1.

Figure 1.

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MR overview of research design and hypotheses. Hypothesis 1: The IVs were SNPs that were strongly associated with air pollution. Hypothesis 2: IVs do not influence the results through confounding factors. Hypothesis 3: IVs affect functional outcome after ischemic stroke only through air pollution exposure and not through other pathways. IVs = instrumental variables, MR = Mendelian randomization, NO2 = nitrogen dioxide, NOx = nitrogen oxide, PM = particulate matter, PRESSO = pleiotropy residual sum and outlier, SNPs = single-nucleotide polymorphisms.

2.2. Data sources for exposure and outcome

We conducted research on air pollutants by reviewing the relevant literature and accessing genome-wide association analysis (GWAS) data. The GWAS data on air pollution were obtained from the UK Biobank. Air pollutants commonly include NOx, NO2, particulate matter (PM), sulfur dioxide, carbon monoxide, and ozone.[17] Furthermore, within the GWAS dataset, PM (PM2.5-10 and PM10) was the most frequently studied pollutant, followed by NO2 and NOx, with limited research on other pollutants. Based on this, we selected the 5 most extensively studied pollutants (NOx, NO2, PM2.5, PM2.5-10, and PM10) for a 2-sample MR study on the relationship between air pollution and functional outcome after ischemic stroke.

We obtained aggregated data on functional outcomes after ischemic stroke from the Genetics of Ischemic Stroke Functional Outcome (GISCOME) network.[18] The ischemic stroke network comprises studies from the United States, Europe, and Australia, involving 6021 patients of European descent. Functional outcomes were assessed using the modified Rankin Scale (mRS) score for ischemic stroke within the past 3 months. Lower mRS scores (0–2) indicate better outcomes, whereas higher scores (3–6) indicate poorer outcomes. In the preliminary analysis, outcomes were adjusted for age, sex, ancestry, and baseline stroke severity, assessed using the National Institutes of Health Stroke Scale (NIHSS). Additionally, we conducted analyses without adjusting for baseline NIHSS scores for comparison. Additionally, as a supplement, we also analyzed the NIHSS without baseline adjustment (lower mRS scores (0–1) indicate better outcomes, while higher scores (2–6) indicate poorer outcomes, N = 4363).

2.3. Genetic IVs selection

To meet the 3 assumptions of the MR analysis, we selected IVs using the following procedure. Initially, all SNPs significantly associated with air pollution were identified and IVs were selected based on a genome-wide significance threshold (P < 5 × 10−8). However, this threshold yielded a limited number of IVs in the present study. Therefore, we adopted a liberal threshold of P < 5 × 10−6. Furthermore, we evaluated, identified, and removed SNPs in linkage disequilibrium states (R2 < 0.001 and >10,000 kb). If the selected SNPs were unavailable in the generated GWAS, proxy SNPs with linkage disequilibrium (r2 = 0.8) were used. Palindromic nucleotide polymorphisms were removed to ensure that the effect on exposure corresponded to the effect on outcome for the same allele. Second, we eliminated level pleiotropy by retrieving residual phenotypes from PhenoScanV2, excluding SNPs corresponding to phenotypes associated with confounding factors and including the remaining SNPs for further analysis. Finally, for each SNP, we determined r2 and F statistics. The F statistic was used to identify a weak IV bias (F = β2/se2). An F-statistic >10 for each SNP indicated that the selected SNP accurately predicted exposure. The purpose of the selection process was to ensure robust and efficient SNP selection for MR analysis.

2.4. MR and sensitivity analyses

The instrumental variable weighted by inverse variance (IVW) method was employed as a key MR analytical approach to assess the impact of air pollutants on functional outcome following ischemic stroke. This method assumes that all SNPs are valid instruments and evaluates their IVW average Wald ratios as the estimation basis. Other analytical methods include the MR-Egger, weighted median, simple, and weighted model approaches. These methodologies serve as supplementary evidence for assessing the impact of air pollutants on functional outcome after ischemic stroke, each distinguished by unique estimation techniques and characteristics.

Sensitivity analyses were conducted to assess heterogeneity and horizontal pleiotropy among the SNPs in the MR analysis. Heterogeneity was assessed using Cochran Q test, (IVW) method, and the MR-Egger method. A P-value >.05 indicates no significant heterogeneity. To evaluate horizontal pleiotropy in MR analysis, we employed both MR-Egger regression and MR pleiotropy residual sum and outlier (MR-PRESSO) tests simultaneously. In both MR-Egger regression and MR-PRESSO global tests, P-values >.05 indicate no significant horizontal pleiotropy. Moreover, MR-PRESSO can also identify outliers in the instrumental variable injections.

Additionally, we performed leave-one-out sensitivity analyses to assess the impact of potentially pleiotropic SNPs on the causal estimates. This involved systematically excluding 1 SNP at a time and recalculating causal effects. These 2 approaches supplemented the MR-PRESSO test to identify and eliminate the potential pleiotropic IVs.

2.5. Statistical analysis

All analyses in the present study were performed using R software (version 4.2.3; R Core Team, R Foundation for Statistical Computing). Data processing was performed using 2-sample MR and MRPRESSO packages. MR estimates were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs).

In our primary analysis, for MR analysis with multiple outcomes, we employed a Bonferroni-corrected significance threshold of P < .01 (α = 0.05/5 outcomes) at the individual outcome level. This adjustment was made to control for the family wise error rate. MR estimates with a P-value below .01 were deemed to be significantly causally associated, while those falling between .01 and .05 suggested evidence for an association. The significance thresholds were predetermined.

3. Results

3.1. Main analyses

According to the traditional inverse variance-weighted method, we did not detect any statistically significant correlation between major air pollutant PM2.5 and functional outcome after ischemic stroke (OR 0.87; 95% CI: 0.26–2.95; P = .825). Similarly, no correlation was found between NOx (OR 0.77; 95% CI: 0.27–2.21; P = .629), NO2 (OR 1.73; 95% CI: 0.61–4.90; P = .304), PM2.5-10 (OR 2.48; 95% CI: 0.28–22.12; P = .416), PM10 (OR 2.54; 95% CI: 0.41–15.87; P = .319), and functional outcome after ischemic stroke (Fig. 2). Other supplementary MR methodologies, such as MR-Egger regression, weighted median, and weighted mode, have also corroborated similar findings; further analysis, not adjusted for stroke severity, supports the aforementioned findings (PM2.5 [OR 0.68; 95% CI: 0.23–1.97; P = .475], NOx [OR 0.52; 95% CI: 0.21–1.32; P = .169], NO2 [OR 1.79; 95% CI: 0.72–4.44; P = .210], PM2.5-10 [OR 1.64; 95% CI: 0.29–9.29; P = .574], and PM10 [OR 2.04; 95% CI: 0.41–10.13; P = .383]; Fig. 3).

Figure 2.

Figure 2.

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The forest plot of MR estimates based on the IVW method is employed to investigate the causal relationship between specific air pollutants and functional outcome following ischemic stroke. IVW = inverse variance-weighted, MR = Mendelian randomization, NO2 = nitrogen dioxide, NOx = nitrogen oxide, PM = particulate matter, SNP = single-nucleotide polymorphism.

Figure 3.

Figure 3.

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The forest plot of MR estimates based on the IVW method is employed to investigate the causal relationship between specific air pollutants and Models without adjustment for baseline stroke severity. IVW = inverse variance-weighted, MR = Mendelian randomization, NO2 = nitrogen dioxide, NOx = nitrogen oxide, PM = particulate matter, SNP = single-nucleotide polymorphism.

3.2. Sensitivity analyses

In this MR analysis, air pollution serves as the exposure factor, with functional outcomes after ischemic stroke as the outcome variable. After removing SNPs related to palindromic, ambiguous, and confounding factors, 52 SNPs related to PM2.5, 22 SNPs related to PM2.5-10, 29 SNPs related to PM10, 76 SNPs related to NOx, and 81 SNPs related to NO2 remained for functional outcomes after ischemic stroke (Tables S1–S5, Supplemental Digital Content 1). The results for functional outcomes after ischemic stroke without adjustment for stroke severity were consistent with the above results.

Following the exclusion of outliers based on our exclusion strategy, both the MR-PRESSO and MR-Egger intercept tests failed to detect evidence of horizontal pleiotropy in MR estimates. Furthermore, no significant heterogeneity was observed between air pollution exposure and functional outcomes after ischemic stroke. Funnel plots, constructed from positive MR estimates obtained from MR-Egger regression, displayed a symmetrical distribution of IVs in Figures S1–S3, Supplemental Digital Content 2, further supporting the absence of significant pleiotropy. Sensitivity analyses conducted on the positive MR results did not identify any influential outliers. Removing individual IVs individually did not significantly alter the pooled MR estimates (Table 1).

Table 1.

Sensitivity analysis for assessing between-SNP heterogeneity (Cochran Q test) and horizontal pleiotropy (MR-Egger intercept test and MR-PRESSO test).

Exposure Outcome nSNP Heterogeneity Horizontal pleiotropy
MR-Egger (P value) IVW (P value) MR-Egger regression (P value) MR-PRESSO globe test (P value)
PM2.5 Functional outcome 52 .873 .881 .465 .892
Functional outcome adj 52 .736 .763 .696 .759
PM2.5-10 Functional outcome 22 .574 .636 .923 .623
Functional outcome adj 22 .162 .186 .567 .164
PM10 Functional outcome 29 .966 .973 .672 .977
Functional outcome adj 29 .797 .766 .223 .806
NOx Functional outcome 76 .946 .953 .667 .952
Functional outcome adj 76 .792 .789 .309 .796
NO2 Functional outcome 81 .975 .979 .659 .978
Functional outcome adj 81 .795 .818 .992 .768
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IVW = inverse variance-weighted, MR = Mendelian randomization, NO2 = nitrogen dioxide, NOx = nitrogen oxide, PM = particulate matter, PRESSO = pleiotropy residual sum and outlier, SNP = single-nucleotide polymorphism.

4. Discussion

Ischemic stroke has a high incidence rate in the population, and the subsequent functional outcome directly impacts the long-term quality of life of patients, thus identifying appropriate treatment and rehabilitation plans is crucial.[8] To explore factors influencing functional outcome after ischemic stroke and to undertake targeted interventions, we employed a 2-sample MR analysis based on large-scale GWAS summary statistics. The study results indicate that air pollutants (such as PM2.5, PM10, PM2.5-10, NOx, and NO2) do not appear to significantly affect functional outcome after ischemic stroke. A sensitivity analysis further validated the reliability of the findings.

In recent years, there has been increasing concern regarding the health hazards associated with air pollution. However, there is currently no high-quality evidence supporting its impact on functional outcome after ischemic stroke.[19] A prospective stroke cohort study observed 12,291 patients with stroke shortly exposed to air pollution-related outcomes poststroke. The results revealed 1649 patients with severe functional issues. Correlation analysis revealed an impact from PM1 and PM2.5, whereas no significant correlation was observed between PM10 and NO2.[20] Another cohort study examined the impact of long-term exposure to air pollution on functional outcome after ischemic stroke, involving 1984 patients admitted due to their first stroke. Among them, 7.2% died, significantly associated with NO2 pollution.[12] Furthermore, a systematic review and meta-analysis encompassing 480 observational studies on the impact of air pollution on poststroke function indicated that current limited evidence does not clearly establish the relationship between PM2.5-10 and poststroke mortality.[21] Thus, different exposure durations and pollutants have varying effects on prognosis. From a mechanistic perspective, key processes in the functional outcome after ischemic stroke include early protease-mediated disruption of the blood-brain barrier,[22] which leads to recruitment of microglia cells and subsequent release of inflammatory factors causing neuronal functional damage,[23] thereby affecting the recovery process. Inflammation plays a crucial role,[24] and while air pollution may indirectly increase the risk of ischemic stroke through mediating inflammatory responses,[25] some studies suggest that inflammatory responses are not entirely destructive. Factors such as IL-1β and tumor necrosis factor may have protective effects on stress response after stroke.[26]

The above research evidence regarding the effects of air pollution on poststroke functional outcome is inconsistent, possibly due to some limitations inherent in the studies themselves, including the inability of observational studies to completely exclude the influence of confounding factors and the inability of animal experiments to guarantee homogeneity with humans; therefore, our study’s strength lies in using strongly correlated instrumental variables for MR analysis, where no heterogeneity or pleiotropy was observed in the analysis, demonstrating robust and reproducible results. It is worth noting that we do not deny the negative impact of air pollution on functional outcome after ischemic stroke, possibly affecting ischemic stroke recovery indirectly through conditions such as hypertension, diabetes, and heart disease.[27-29] Furthermore, besides PM2.5, PM10, PM2.5-10, NOx, and NO2, other air pollutants (such as sulfur dioxide, nitrogen monoxide, ozone, etc) may have potential effects that require further research for validation.[30]

However, the current study had some limitations. First, our study is limited to analysis of a single European ancestry population, which may restrict the generalizability of our results to other populations. Second, the limited number of effective instrumental variables may affect our estimation of causal relationships. Lastly, the relatively few types of pollutants chosen may make it difficult to fully elucidate the relationship between air pollution and functional outcome after ischemic stroke. Additionally, the GISCOME database lacks outcome data for specific stroke subtypes, which also hinders our assessment of the impact of exposure to air pollution on functional outcome in different stroke subtypes. Considering these limitations, larger-scale and more comprehensive studies on air pollutants are necessary to further validate the current research findings and provide more guiding clinical treatment and rehabilitation recommendations.

5. Conclusion

In summary, there may not be a direct causal relationship between air pollution (PM2.5, PM10, PM2.5-10, NOx, and NO2) and functional outcome after ischemic stroke.

Acknowledgments

We would like to express our sincere gratitude to the databases that provided open GWAS data (IEU Open GWAS Project and Psychiatric Genomics Consortium). Your contributions are invaluable to the advancement of medical research.

Author contributions

Conceptualization: Yunsen Zhang, XiaoWei Liu, YuanHong Ge, RongHua Xu, Xuejun Xu.

Data curation: Yunsen Zhang, XiaoWei Liu, YuanHong Ge, Xuejun Xu.

Formal analysis: Yunsen Zhang, XiaoWei Liu, YuanHong Ge, Xuejun Xu.

Funding acquisition: Yunsen Zhang, RongHua Xu, Xuejun Xu.

Investigation: Yunsen Zhang, Xuejun Xu.

Methodology: Yunsen Zhang, XiaoWei Liu, YuanHong Ge, Xuejun Xu.

Project administration: Yunsen Zhang, RongHua Xu, Xuejun Xu.

Resources: Yunsen Zhang, XiaoWei Liu, YuanHong Ge, RongHua Xu.

Software: Yunsen Zhang, Yong Liu, Xuejun Xu.

Supervision: Yong Liu.

Validation: Yunsen Zhang, XiaoWei Liu, Yong Liu, Xuejun Xu.

Visualization: Yunsen Zhang.

Writing – original draft: Yunsen Zhang, YuanHong Ge.

Writing – review & editing: Yunsen Zhang.

medi-105-e50160-s001.xlsx (14.5KB, xlsx)
medi-105-e50160-s002.tif (27.8MB, tif)
medi-105-e50160-s003.tif (22.9MB, tif)
medi-105-e50160-s004.tif (51.7MB, tif)
medi-105-e50160-s005.xlsx (11.6KB, xlsx)
medi-105-e50160-s006.xlsx (12.2KB, xlsx)
medi-105-e50160-s007.xlsx (16.5KB, xlsx)
medi-105-e50160-s008.xlsx (17.1KB, xlsx)

Abbreviations:

GWAS
genome-wide association analysis
IVW
inverse variance-weighted
MR
Mendelian randomization
NO2 =
nitrogen dioxide
NOx =
nitrogen oxide
PM
particulate matter
PRESSO
pleiotropy residual sum and outlier
SNP
single-nucleotide polymorphism

This research was supported by the third batch of the Major Science and Technology Application Demonstration Project (2019-YF09-00097-SN) of Chengdu.

The data were obtained from a publicly accessible database, and no human subjects were involved; therefore, the ethical parameters were not applicable.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available. 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 in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000050160).

How to cite this article: Zhang Y, Liu X, Ge Y, Liu Y, Xu R, Xu X. Causal association between air pollution and functional outcome after ischemic stroke: Evidence from 2-sample MR study. Medicine 2026;105:32(e50160).

YSZ, XWL, and YHG contributed to this article equally.

Contributor Information

YunSen Zhang, Email: 17396222052@163.com.

XiaoWei Liu, Email: 807573491@qq.com.

YuanHong Ge, Email: lai2008ge@sina.cn.

Yong Liu, Email: 807573491@qq.com.

RongHua Xu, Email: xu2003xuejun@sina.com.

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Supplementary Materials

medi-105-e50160-s001.xlsx (14.5KB, xlsx)
medi-105-e50160-s002.tif (27.8MB, tif)
medi-105-e50160-s003.tif (22.9MB, tif)
medi-105-e50160-s004.tif (51.7MB, tif)
medi-105-e50160-s005.xlsx (11.6KB, xlsx)
medi-105-e50160-s006.xlsx (12.2KB, xlsx)
medi-105-e50160-s007.xlsx (16.5KB, xlsx)
medi-105-e50160-s008.xlsx (17.1KB, xlsx)

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