Causal inference study on the influence of anemia indicators on the incidence of allergic rhinitis: two- sample Mendelian randomization

Causal inference study on the influence of anemia indicators on the incidence of allergic rhinitis: two- sample Mendelian randomization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal inference study on the influence of anemia indicators on the incidence of allergic rhinitis: two- sample Mendelian randomization YanNi Chen, Song ChenFei, Yu HaiDong, Liu Tao, Han XinMin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4244796/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Anemia and allergic rhinitis (AR) are prevalent clinical conditions in children. Previous studies have suggested an association between anemia and AR. Using Mendelian randomization (MR) analysis, we investigated causality with regard to the influence of anemia indicators on the incidence of AR. We searched the IEU OpenGWAS database for summary information on genome-wide association studies (GWAS) of anemia and AR in European populations. Three methods, MR-Egger, weighted median (WM), and inverse variance weighted (IVW), were utilized to evaluate the causal link between SLE and thyroid disease. We assessed pleiotropy and heterogeneity using MR-Egger intercept, MR-PRESSO, and Cochran’s Q test, among others. The IVW model revealed a significant causal association between anemia and AR. As the severity of anemia increased (OR = 1.02, p < 0.05), the risk of AR also increased. Moderate heterogeneity was observed (Cochran’s Q p-value I 2 > 50%) among significant anemia-associated indicators in the MR results of AR (IVW). The MR-Egger regression was used to assess the horizontal pleiotropy of the instrumental variables. The statistical hypothesis test P-values of the intercept terms of each index exceeded 0.05, and the intercept was close to zero, indicating that the causal inference in this study was unaffected by horizontal pleiotropy. The MR analysis results support a potential causal link between anemia and AR, suggesting that anemia is a risk factor for AR. These insights could contribute towards raising awareness regarding the pathogenesis of AR and aid the formulation of strategies for its prevention, treatment, and prognosis. anemia allergic rhinitis Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The incidence of allergic rhinitis (AR) has been on the rise with societal development. In Europe, perennial (or persistent) AR accounts for approximately 29% of all cases of AR [ 1 ]. Children are particularly susceptible to AR, a common chronic illness [ 2 ]. As widely known, AR presents in various ways. Specific antibodies bind to high-affinity immunoglobulin E (IgE) receptors on the surface of mast cells in the nasal mucosa. Upon exposure to allergens, these antibodies cross-link, leading to mast cell degranulation and the release of histamine, prostaglandins, and other inflammatory mediators [ 3 ]. Therefore, there is an urgent need to develop preventive strategies to mitigate AR. The prevalence of anemia is estimated to be 24.8% in the general population [ 4 ]. Previous epidemiological studies have reported an increased risk of anemia in individuals with allergic disorders [ 5 ]. However, most of these studies are clinical observational studies, which may be influenced by various biases, confounding factors, and reverse causation interference. Mendelian randomization (MR) analysis is a powerful method that utilizes genetic variation as an instrumental variable to elucidate causal relationships between risk factors and diseases. According to Mendel's law of independent assortment, genetic variants are independent of each other and typically unaffected by other factors or confounding variables, enhancing the reliability of correlations in MR studies compared to randomized clinical trials. However, to the best of our knowledge, thus far, no MR study has explored the association between anemia and AR, which is the focus of our study. To assess the potential causal link between anemia and AR, we employed various two-sample MR methods, primarily utilizing the inverse variance-weighted (IVW) method. We conducted sensitivity analyses, including tests for heterogeneity, pleiotropy, and one-by-one elimination, to ensure the robustness of our findings. We simultaneously used smoking and environmental pollution as variables; these demonstrated significant causal relationships with both AR and anemia as the exposure factors in a subsequent multivariate MR model of AR. The causal relationship between anemia and AR is expected to provide new therapeutic directions for future research on AR, offering an effective strategy for AR prevention. 2. Materials and Methods 2.1 Study reporting guidelines and study design Two-sample MR and public datasets were utilized to examine the impact of anemia indicators on AR. Study reporting adhered to the Strengthening the Reporting of Observational Studies in Epidemiology using MR (The STROBE-MR Statement) [ 6 ]. A schematic of the study design is depicted in Fig. 1 . 2.2 Data sources Genome-wide association study (GWAS) data for the anemia indicators GWAS IDs (finn-b-D3_ANAEMIANAS) were acquired from the MRC IEU OpenGWAS database, and standardized association summary statistics were generated using the R package TwoSampleMR. The study population comprised Finnish individuals, totaling 218,167 samples, including 5,929 cases of anemia and 212,238 controls. GWAS data on AR were obtained, with the GWAS ID for AR (ukb-a-254) sourced from the MRC IEU OpenGWAS database. Standardized association summary statistics were generated using the R package TwoSampleMR. The study population consisted of Europeans, totaling 83,529 samples, with 18,934 cases of AR and 64,595 control participants. We initially identified two risk factors associated with AR from the literature [ 7 , 8 ]: smoking and air pollution. For smoking, we retrieved 58 smoker-related traits by searching the MRC IEU OpenGWAS database using "smoke" as the keyword (Table S1 ). Summary statistics of the associations for these 58 smoking indicators were obtained and standardized using the R package TwoSampleMR. Similarly, for air pollution, we obtained 50 smoking-related indicators by searching air pollution-related traits from the MRC IEU OpenGWAS database using "Air pollution" as the keyword (Table S2 ). The R package TwoSampleMR was used to obtain and standardize the association summary statistics of these 50 smoking indicators. 2.3 Instrumental variable selection A valid instrumental variable for genetic variation must satisfy three core assumptions: (1) the hypothesis of association, meaning the selected instrumental variable must have a significant relationship with the exposure factor; (2) the independence assumption, ensuring the instrumental variable is not significantly associated with potential confounders that may influence both the exposure and outcome; (3) the exclusivity limitation, indicating that the instrumental variable can only influence the outcome through the pathway of "instrumental variable → exposure → outcome." In this study, the instrumental variable screening criteria for exposure were as follows. Single nucleotide polymorphisms (SNPs) with a P-value < 5 × 10 − 6 in GWAS served as the primary screening criterion. SNPs in linkage disequilibrium (r 2 10,000 kb between each gene) were excluded. Instrumental variables from the GWAS of the outcome data were extracted based on the selected SNPS. F-statistics were calculated to assess weak instrumental bias. An F-statistic below 10 indicates weak instrument strength, potentially biasing the results [ 9 ]. Therefore, such variables were removed to mitigate result distortion. The F-statistic was calculated as follows: F= \(\frac{N-k-1}{k}\) × \(\frac{{R}^{2}}{1-{R}^{2}}\) where n is the sample size, k is the number of instrumental variables used, and R 2 reflects the extent to which the instrumental variables explain exposure. R 2 = 2 × (1-MAF) × MAF × 2 β, where MAF is the minimum allele frequency and β is the allele effect size. 2.4 MR causal effect estimation Multiple two-sample MR methods have been used to evaluate causal effects between exposures and outcomes, including inverse variance-weighted (IVW) [ 10 ], weighted median (WM) [ 11 ], MR-Egger [ 12 ], weighted mode [ 13 ], and simple mode. Some studies have demonstrated [ 11 ] that the IVW method exhibits slightly greater robustness under specific conditions; it disregards the intercept term in regression and employs the inverse of the outcome variance as the weight for fitting. Therefore, in the absence of pleiotropy and with or without heterogeneity, the IVW method served as the primary MR analysis, supplemented by the other four methods (with the IVW random-effects model employed in the presence of heterogeneity). The MR-Egger method was utilized to estimate results in the presence of pleiotropy. Reverse causality was assessed for potential causal effects of exposure outcomes using the same methodology. 2.5 Sensitivity analysis Sensitivity analysis of the results was performed using the heterogeneity, pleiotropy, and one-by-one exclusion tests, as outlined below: (1) Heterogeneity test: Cochran’s Q test was employed to assess heterogeneity among the SNP estimates. A statistically significant Cochran’s Q test indicated significant heterogeneity in the results. While Cochran’s Q test determined only the presence or absence of heterogeneity, it could not ascertain the distribution of heterogeneity. Therefore, the I 2 statistic was used to indicate the proportion of heterogeneous instrumental variables in the total variation: when I 2 ≤ 0, it was considered as 0, indicating no observed heterogeneity. I 2 = 0–25%, mild heterogeneity; I 2 = 25–50%, moderate heterogeneity; I 2 > 50%, high heterogeneity. The calculation formula is as follows: I 2 = \(\frac{Q-df}{Q}\) ×100% (2) Pleiotropy test: The MR-Egger method was used to examine the pleiotropy of instrumental variables. A P-value less than 0.05 for the MR-Egger's intercept indicated significant horizontal pleiotropy of genetic variation. (3) Leave-one-out test: The MR results were recalculated by excluding individual SNPs one at a time to assess whether the SNP affected the association between anemia and AR. A substantial difference between the MR effect estimation and the total effect estimation following the exclusion of an instrumental variable indicated sensitivity of the MR effect estimation to that SNP. 2.6 Multivariate MR analysis To perform univariate MR analysis, all smoking and air pollution-related indicators retrieved from the MRC IEU OpenGWAS database were assessed against AR outcomes. A selection of IVW models with significant causal relationships was screened for presentation. Subsequently, the IVW model with significant causality from the initial screening was subjected to reverse Mendelian analysis. Finally, these significant findings were used in multivariate MR analysis to assess the direct effect of anemia on AR. 2.7 Statistical analysis All data calculations and statistical analyses were conducted using R ( https://www.r-projec t.org/, version 4.3.1). The TwoSampleMR package was primarily used for MR analysis [ 14 ]. Cochran’s Q test and leave-one-out analysis were used to evaluate the robustness and reliability of the results. A genetic pleiotropy test was performed using the MR-Egger intercept method. Evaluation metrics included odds ratios (OR) and 95% confidence intervals (95% CI). All statistical P-values were two-sided. For SNPs derived from GWAS studies, P < 5 × 10 − 6 was considered statistically significant. For other statistical tests, statistical significance was set at P < 0.05. 3. Results 3.1 Analysis framework and flow chart 3.2 Instrumental variable screening According to the screening criteria for instrumental variables in this study, SNPs with linkage disequilibrium were excluded. SNPs associated with to anemia indicators were included as instrumental variables after matching with the GWAS data of AR. The number of instrumental variables for anemia is presented in Table 1 . The MR analysis yielded significant results (p < 0.05), and the F-test statistics for the instrumental variables of anemia were all greater than 10. This indicated that the majority of the SNPs screened in this study were strong instrumental variables, and any potential bias due to weak instrumental variables was limited. Table 1 Screening of instrumental variables for anemia and allergic rhinitis and F test results on the strength of instrumental variables Exposure Number of SNPs Median of F Minimum of F Maximum of F Other and unspecified anemias 10 22.92363 32.79645 20.91405 SNPs, single nucleotide polymorphisms; F, F-statistics. 3.3 MR causal effect estimates We employed MR-Egger, weighted median, IVW, simple mode (SM) and weighted mode models for the analysis. Scatter plots of the SNP effect estimates are shown in Fig. 2 . It is apparent that the fitting curves of the scatter plots for all five models exhibit consistent directions, with relatively consistent slopes observed across most models. Additionally, the intercept of the IVW model closely approximates 0. The estimation results of the causal effect of anemia on the incidence of AR are shown in Table 2 . The results of the IVW model indicate a significant causal association between anemia and AR. Specifically, more severe anemia was associated with a higher risk of AR (OR = 1.02, p < 0.05). Table 2 Mendelian randomization causal effect estimates of anemia on the onset of allergic rhinitis Exposure Method nsnp Number of SNPs Beta Standard error OR95CI p-value Other and unspecified anemias Inverse variance weighted 10 0.020549 0.010396 1.020762 1.020762 (1.000173, 1.041775) 0.048088 Other and unspecified anemias MR Egger 10 0.000776 0.026513 1.000776 0.977368 (0.950099, 1.054156) 0.977368 Other and unspecified anemias Simple mode 10 0.01153 0.012941 1.011596 0.396149 (0.986261, 1.037583) 0.396149 Other and unspecified anemias Weighted median 10 0.012099 0.010181 1.012172 1.012172 (0.992176, 1.032572) 0.234659 Other and unspecified anemias Weighted mode 10 0.01153 0.012018 1.011596 0.36243 (0.988047, 1.032572) 0.36243 SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval. 3.4 Sensitivity analysis The heterogeneity of the significant results was assessed using Cochran’s Q test and I 2 statistics, as shown in Table 3 . The results showed moderate heterogeneity (Cochran’s Q p-value I 2 > 50%) among significant anemia indicators in the MR results of AR (IVW). A funnel plot of the instrumental variable of anemia (Fig. 3 ) showed that the scatter points of the causal association effect were symmetrical, indicating no potential bias in the results. For indicators with highly heterogeneous results in the heterogeneity test, the random-effects model of IVW was used to estimate the causal effects, and the results are shown in Fig. 4 and Table 4 . Table 3 Mendelian randomization analysis heterogeneity test for the association between Anaemia and Allergic Rhinitis Exposure method Q Q df Q pvalue I 2 (%) Other and unspecified anaemias MR Egger 16.34993 8 0.037637 0.510701 Inverse variance weighted 17.70284 9 0.038782 0.491607 Q, Cochran's Q test statistic;Q df, degrees of freedom for the Q test; I 2 statistic reflects the proportion of heterogeneity attributed to instrumental variables in the total variability. Table 4 Inverse variance weighted random effects model analysis of the association between anemia and allergic rhinitis Exposure Number of SNPs Beta Standard error p-value Other and unspecified anemias 10 0.020549 0.010396 0.048088 SNPs: single nucleotide polymorphisms The MR-Egger regression was used to test the horizontal pleiotropy of the instrumental variables. The statistical hypothesis test P-values of the intercept terms of each index exceeded 0.05, and the intercept was close to 0, indicating that the causal inference in this study was not influenced by horizontal pleiotropy (Table 5 ). Table 5 Mendelian randomization analysis of anemia’s impact on allergic rhinitis using a horizontal pleiotropy test Exposure MR-Egger intercept Standard error p-value Other and unspecified anemias 0.003878 0.004766 0.439405 Sensitivity analysis of the results was conducted using the elimination test individually (Fig. 5 ). Each line in the figure represents the effect size and 95% confidence interval range of the index after the corresponding SNP is removed, with the red line representing the reference effect interval. Each line closely overlaps with the red-line interval, indicating that the effect estimate remains stable even with the removal of a single SNP. This suggests that the results are robust. The results of the reverse causality MR analysis showed no significant causal effect of AR on the anemia index (p-value > 0.05), as shown in Fig. 6 . 3.5 Multivariate MR analysis We conducted a univariate MR analysis of all smoking- and air pollution-related indicators retrieved from the MRC IEU OpenGWAS database and the outcomes of AR, respectively. Significant causal relationships were identified using a combination of IVW models (Table 6 ). The results indicated significant causal associations among smoking [cigarettes smoked per day (GWAS ID: ieu-b-142), cigarettes smoked per day (GWAS ID: ieu-b-4826)], type of tobacco previously smoked [manufactured cigarettes (GWAS ID: ukb-d-2877_1)], air pollution [particulate matter air pollution (pm10); 2010 (GWAS ID: ukb-e-24005_EAS)], and other indicators. A reverse Mendelian analysis was then performed on the combinations with significant causal relationships in the previously screened IVW model. The results of reverse causality MR analysis showed that AR had a significant effect on smoking [cigarettes smoked per day (GWAS ID: ieu-b-142), cigarettes smoked per day (GWAS ID: ieu-b-4826)], type of tobacco previously smoked [manufactured cigarettes (GWAS ID: ukb-d-2877_1)], and particulate matter air pollution [(pm10), 2010 (GWAS ID: ukb-e-24005_EAS)]. Other indicators had no causal effect (p > 0.05), and the results are shown in Table 7 . Table 6 Causal effect estimates of Mendelian randomization for smoke, air pollution, and allergic rhinitis Exposure Outcome Number of SNPs Beta Standard error OR95CI p-value Cigarettes smoked per day (GWAS ID: ieu-b-142) Doctor diagnosed hay fever or allergic rhinitis 68 0.015890 0.006535 0.984235 (0.971708, 0.996924) 0.015043 Cigarettes smoked per day (GWAS ID: ieu-b-4826) Doctor diagnosed hay fever or allergic rhinitis 19 0.003301 0.001456 0.996704 (0.993863, 0.999552) 0.023366 Type of tobacco previously smoked: manufactured cigarettes (GWAS ID: ukb-d-2877_1) Doctor diagnosed hay fever or allergic rhinitis 16 0.168094 0.071974 0.845274 (0.734061, 0.9733371) 0.019518 Particulate matter air pollution (pm10); 2010 (GWAS ID: ukb-e-24005_EAS) Doctor diagnosed hay fever or allergic rhinitis 6 0.014345 0.004838 1.014448 (1.004874, 1.024113) 0.003026 SNP, single nucleotide polymorphism; β, the effect coefficients in Mendelian randomization analysis; OR, odds ratio; CI, confidence interval. Table 7 Reverse Mendelian randomization analysis of smoking (smoke) and air pollution (air pollution) on allergic rhinitis Outcome Exposure Number of SNPs Beta Standard error p-value Cigarettes smoked per day (GWAS ID: ieu-b-142) Cigarettes smoked per day 46 0.01349 0.086104 0.875502 Cigarettes smoked per day (GWAS ID: ieu-b-4826) Cigarettes smoked per day 37 0.00823 0.875349 0.992498 Type of tobacco previously smoked: manufactured cigarettes (GWAS ID: ukb-d-2877_1) Type of tobacco previously smoked: manufactured cigarettes 48 0.00463 0.017885 0.795819 Particulate matter air pollution (pm10); 2010 (GWAS ID: ukb-e-24005_EAS) Doctor diagnosed hay fever or allergic rhinitis 38 0.45725 0.414422 0.269879 SNP, single nucleotide polymorphism; β, the effect coefficients in the Mendelian randomization analysis. Finally, a multivariate MR Analysis of AR was conducted separately using these significant results as exposures to assess the direct effect of anemia on AR (Table 8 ). Models 1, 2, and 3, adjusted for cigarettes smoked per day (GWAS ID: ieu-b-142), cigarettes smoked per day (GWAS ID: ieu-b-4826), type of tobacco previously smoked, and manufactured cigarettes (GWAS ID: ukb-d-2877_1), revealed no significant effect of anemia on AR. Model 4, adjusted for particulate matter air pollution [(pm10); 2010 (GWAS ID: ukb-e-24005_EAS)], indicated that anemia still exerted a significant direct effect on AR. Table 8 Results of multivariate Mendelian randomization analysis of the effects of smoking, air pollution, and anemia on the incidence of allergic rhinitis Model Exposure Outcome Beta Standard error p- value Model 1 Other and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.010220 0.007825 0.191556 Cigarettes smoked per day (GWAS id: ieu-b-142) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.018907 0.006319 0.002771 Model 2 Other and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.012454 0.010209 0.222482 Cigarettes smoked per day (GWAS id: ieu-b-4826) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.003668 0.001390 0.008295 Model 3 Other and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.017658 0.009310 0.057864 Type of tobacco previously smoked: manufactured cigarettes (GWAS id: ukb-d-2877_1) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.120096 0.093594 0.199436 Model 4 Other and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.0302924 0.013059 0.020361 Particulate matter air pollution (pm10); 2010 (GWAS id: ukb-e-24005_EAS) Doctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254) 0.0137253 0.006996 0.049762 SNP, single nucleotide polymorphism; β, the effect coefficients in the multivariate Mendelian randomization analysis. 4. Discussion AR, a nasal mucosal allergic disease, manifests with symptoms such as nasal itching, congestion, sneezing, and clear mucus. Long-term persistence significantly affects patients’ sense of smell, sleep, and overall quality of life. Despite ongoing research, the pathogenesis and underlying mechanisms of AR have not yet been fully elucidated. This study utilized datasets from the public database MRC IEU OpenGWAS to investigate the causal relationship between anemia and AR through two-sample MR. Various two-sample MR methods were used to evaluate exposure-outcome causal effects, and sensitivity, Cochran’s Q, and leave-one-out analyses were conducted to ensure result robustness. The MR Egger intercept method was used to conduct genetic pleiotropy testing, validating the reliability of the findings. In the IVW model, MR estimation revealed a significant causal relationship between anemia severity and AR onset. The more severe the anemia (OR = 1.02, p < 0.05), the higher the AR risk. All smoking- and air pollution-related indicators retrieved from the MRC IEU OpenGWAS database were subjected to univariate MR analysis with significant causal relationships in the IVW model. The results showed that smoking and air pollution had significant causal relationships with AR. Subsequently, a reverse Mendelian analysis was performed on the combinations with significant causal relationships in the IVW model selected earlier. The results of the reverse causal MR analysis showed that AR had no causal effect on smoking or other indicators (p > 0.05). Anemia is a medical condition characterized by reduced levels of hemoglobin and a deficiency in red blood cell mass, which results in the inability to transport oxygen to peripheral tissues [ 16 , 17 ]. The two major subtypes of anemia are iron deficiency anemia and inflammatory anemia. Anemia is prevalent in children and may lead to impaired cognitive and motor development [ 18 , 19 ]. Iron participates in energy metabolism, and, as it exists in many forms in the human body, its physiological functions are correspondingly extensive. For example, hemoglobin can transport oxygen, myoglobin can store oxygen, and cytochrome can transport electrons, combine with various enzymes to decompose peroxides, detoxify bacteria, and participate in the tricarboxylic acid cycle. Iron deficiency reduces the bactericidal ability of neutrophils and impairs lymphocyte function; however, iron supplementation can improve the immune function. The serum ferritin level and leukocyte bactericidal ability decreased, and peripheral blood T lymphocyte subset cell function was impaired. Elevated serum ferritin levels may promote an inflammatory response by mediating T lymphocyte immune imbalance, which leads to the occurrence and development of AR. According to the findings of Japanese researchers, allergic diseases are related to caregiver-reported anemia in children. Considering the high risk of negative effects on growth/development and quality of life in children with anemia, increased clinician awareness regarding screening and monitoring of anemia may relieve the disease burden in children with allergic diseases. In this cohort analysis of the largest birth cohort in Japan, it was found that anemia at 3 years of age is more prevalent in children with caregiver-reported allergic conditions than in children without allergies [ 15 ]. The etiology of the increased risk of anemia in children with allergic diseases remains unclear. Management of anemia in children with allergies is paramount, given its potential to impede growth and development. In addition to nutritional interventions for children with food allergies, increased clinical vigilance in the screening and monitoring of anemia in this population may alleviate the disease burden associated with both conditions. Compared to traditional epidemiological studies, the advantage of this study is that it explored the causal relationship between anemia and AR using two-sample MR. This method uses randomly assigned genotypes as instrumental variables to infer the impact of exposure factors on outcomes, greatly reducing the impact of reverse causal relationships and confounding factors. Multiple MR analysis methods were used in this study to ensure the reliability and stability of the results. However, this study has certain limitations. First, the outcome indicator of AR was not further classified to explore the causal relationship between anemia and different types of AR. It should be included in the dataset of different types of AR for analysis to improve the accuracy of conclusions. Second, the summary data used in this study came from a European population, and the conclusions drawn lack universality. Therefore, data from different ethnic groups should be included for supplementary verification. Finally, this study only explored the relationship between anemia and AR from a genetic perspective, but its specific mechanism of action needs further research and verification. Our study confirmed a significant causal relationship between anemia and AR. This suggests that anemia is a risk factor for AR. Elevated serum ferritin levels may promote an inflammatory response by mediating T lymphocyte immune imbalance, which leads to the occurrence and development of AR. How this affects future research on AR and the specific action mechanism still needs to be tested. Our results provide a platform for researchers to explore the relationship between anemia and AR. Future studies should elucidate the mechanism underlying the association between anemia and AR, or explore novel treatment modalities to prevent or treat AR. Declarations Acknowledgements : All authors agree to submit their papers to the journal for publication. We appreciate Han XinMin for support and assistance in this study. Funding : This study was funded by Shanghai Baoshan District Medical Key Specialty Class B: Community Integrated Chinese and Western Medicine Paediatrics Construction(BSZK-2023-BZ09 ) Author’s contributions ：All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chen YanNi, Song ChenFei and Yu haidong. The first draft of the manuscript was written by Liu Tao .Han XinMin approved the final manuscript. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Compliance with Ethical Standards : The data sources employed in our research were obtained from publicly available databases; therefore, securing approval from an ethics committee was considered unnecessary. Ethical approval ：This article contains no studies with human or animal subjects performed by any of the authors. Conflicts of interest : Chen YanNi, Song ChenFei, Yu HaiDong, Liu Tao, Han XinMin all declare no conflicts of interest. Data availability : The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. References Calderon MA, Carr VA, Jacobson M, Sheikh A, Durham S. Allergen injection immunotherapy for perennial allergic rhinitis. Cochrane Database Syst Rev . 2019;2019(1):CD007163. Published 2019 Jan 8. doi:10.1002/14651858.CD007163.pub2 Schwarz C, Eschenhagen P, Schmidt H, et al. 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Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: A systematic analysis of populationrepresentative data. Lancet Glob. Health 2013, 1, e16–e25. Additional Declarations No competing interests reported. Supplementary Files TableS1smokeid.csv TableS2Airpollutionid.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4244796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290527816,"identity":"658c6a6d-3f20-4502-a283-88484c1bb8ce","order_by":0,"name":"YanNi Chen","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"YanNi","middleName":"","lastName":"Chen","suffix":""},{"id":290527817,"identity":"aef84d3b-d1c1-4bc1-bf85-74c14682643c","order_by":1,"name":"Song ChenFei","email":"","orcid":"","institution":"Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"ChenFei","suffix":""},{"id":290527818,"identity":"93bb5962-78ef-4a9d-b25b-a04145e7292d","order_by":2,"name":"Yu HaiDong","email":"","orcid":"","institution":"Youyi Road Community Health Service Centre for Baoshan District","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"HaiDong","suffix":""},{"id":290527819,"identity":"8e478408-e58b-4dc0-8414-11685149c509","order_by":3,"name":"Liu Tao","email":"","orcid":"","institution":"Youyi Road Community Health Service Centre for Baoshan District","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Tao","suffix":""},{"id":290527820,"identity":"037bbb07-3c2d-4433-b992-f477247145dd","order_by":4,"name":"Han XinMin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACNvmDjY9/VNjI8bM3H3yQUFFDWAufBHOzMcOZNGPJnmPJBg/OHCOsRU6CvU2ase1wosGNHDPJhy3MRDhMurFNurDtcIJkQ1paRWIDGwN/e3cCfi0yB5utZ5xLz+NnOHzsRuIOGQaJM2c34NfCkNh4g6fMuliysS3tRuIZNgYDiVyCWhokeNiYEzcc5jErSGxjJkKLRGKTNE+bc+KGYzxmDMRp4TnYbDgDHMhsyRIJZ47xEPSLfHv7wwcfQFEp//jgxx8VNXL87b34tWAAHtKUj4JRMApGwSjACgBqXE4c7TpdHQAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Han","middleName":"","lastName":"XinMin","suffix":""}],"badges":[],"createdAt":"2024-04-10 03:18:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4244796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4244796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55007332,"identity":"0935ac65-db5a-472d-bace-8ca2be97b7c5","added_by":"auto","created_at":"2024-04-19 19:00:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167520,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of Mendelian randomization analysis. a. The fundamental assumptions of Mendelian randomization analysis include: (1) the association assumption, that is, the selected instrumental variables must exhibit a significant relationship with the exposure factor; (2) exclusivity limitation, that is, the instrumental variable should only influence the outcome through the pathway of \"instrumental variable → exposure → outcome;\"; (3) independence assumption, that is, the instrumental variable must not be significantly associated with potential confounders that might affect the exposure or outcome. b. Flow chart outlining the analysis methods employed in this study. SNPs, single nucleotide polymorphisms; MR, Mendelian randomization; IVW, inverse variance weighted; MR-Egger, Mendelian randomization-Egger; GWAS, genome-wide association study\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/0944962f0faa1930a740bf24.png"},{"id":55008727,"identity":"642c2585-e37a-4b67-a640-227c8509b2dc","added_by":"auto","created_at":"2024-04-19 19:08:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91828,"visible":true,"origin":"","legend":"\u003cp\u003eEffect estimates of different models for the Mendelian randomization analysis of anemia indicators and allergic rhinitis. The Scatter plot shows the causal relationship between anemia and AR, and the slope of the line indicates the magnitude of the causal relationship predicted by the different models\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/85d750583c23546be9ede047.png"},{"id":55007331,"identity":"3e89b494-ac62-47b6-81fb-a4914c5c7c6f","added_by":"auto","created_at":"2024-04-19 19:00:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35951,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of heterogeneity test for the Mendelian randomization analysis of anemia indicators and allergic rhinitis. Funnel plots showing the causal effect estimates for each instrumental variable of anemia and AR and the causal effect estimates for inverse variance weighted and MR-Egger’s model are marked with lines on the plot\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/bcb56c41fd60e69fc52b1ba6.png"},{"id":55007336,"identity":"fb8bd457-5fb2-4e99-a620-825bf5909242","added_by":"auto","created_at":"2024-04-19 19:00:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71692,"visible":true,"origin":"","legend":"\u003cp\u003eEffect estimates of random effects model of Mendelian randomization analysis IVW for anemia indicators and allergic rhinitis. The scatter plot shows the causal relationship between anemia and allergic rhinitis, and the slope of the line indicates the magnitude of the causal relationship predicted by the IVW random-effects model\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/8fc742fc10a1fcd33e4742f5.png"},{"id":55007335,"identity":"1c028ee0-3e54-416c-aebd-1a229c3ee797","added_by":"auto","created_at":"2024-04-19 19:00:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73354,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot depicting the results of the one-by-one exclusion test for the Mendelian randomization analysis of the impact of anemia on AR\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/b66d9e4994b8ecc3c1a46815.png"},{"id":55008728,"identity":"7d28f931-7fba-4144-a8e1-9b70685e9499","added_by":"auto","created_at":"2024-04-19 19:08:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":28020,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple model analysis results in reverse causality Mendelian randomization analysis of anemia indicators on allergic rhinitis. The forest plot shows the reverse causal association analysis results of multiple Mendelian randomization models for AR and anemia indicators. The effect estimates are presented as OR and 95% CI, and the number of instrumental variables used by each model, as well as the calculated beta values and standard errors, are shown. Abbreviations: SNPs, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/5f3ee302e5a71e8d6d156cb4.png"},{"id":55010477,"identity":"6f0d4eca-b08a-428f-9316-70d3ae7a1d55","added_by":"auto","created_at":"2024-04-19 19:24:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":876359,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/da5448b5-b71e-4753-89ae-3395eaed636c.pdf"},{"id":55007338,"identity":"04e42055-8436-4182-880f-f4d03c99241c","added_by":"auto","created_at":"2024-04-19 19:00:11","extension":"csv","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":11708,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1smokeid.csv","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/d9cb130d96c283c9e641814d.csv"},{"id":55007337,"identity":"87e1f84f-1636-449f-907b-75060bbedc04","added_by":"auto","created_at":"2024-04-19 19:00:10","extension":"csv","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":10866,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2Airpollutionid.csv","url":"https://assets-eu.researchsquare.com/files/rs-4244796/v1/9bf05e34f497236cdb05d746.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal inference study on the influence of anemia indicators on the incidence of allergic rhinitis: two- sample Mendelian randomization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe incidence of allergic rhinitis (AR) has been on the rise with societal development. In Europe, perennial (or persistent) AR accounts for approximately 29% of all cases of AR [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Children are particularly susceptible to AR, a common chronic illness [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As widely known, AR presents in various ways. Specific antibodies bind to high-affinity immunoglobulin E (IgE) receptors on the surface of mast cells in the nasal mucosa. Upon exposure to allergens, these antibodies cross-link, leading to mast cell degranulation and the release of histamine, prostaglandins, and other inflammatory mediators [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, there is an urgent need to develop preventive strategies to mitigate AR. The prevalence of anemia is estimated to be 24.8% in the general population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous epidemiological studies have reported an increased risk of anemia in individuals with allergic disorders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, most of these studies are clinical observational studies, which may be influenced by various biases, confounding factors, and reverse causation interference.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis is a powerful method that utilizes genetic variation as an instrumental variable to elucidate causal relationships between risk factors and diseases. According to Mendel's law of independent assortment, genetic variants are independent of each other and typically unaffected by other factors or confounding variables, enhancing the reliability of correlations in MR studies compared to randomized clinical trials. However, to the best of our knowledge, thus far, no MR study has explored the association between anemia and AR, which is the focus of our study.\u003c/p\u003e \u003cp\u003eTo assess the potential causal link between anemia and AR, we employed various two-sample MR methods, primarily utilizing the inverse variance-weighted (IVW) method. We conducted sensitivity analyses, including tests for heterogeneity, pleiotropy, and one-by-one elimination, to ensure the robustness of our findings. We simultaneously used smoking and environmental pollution as variables; these demonstrated significant causal relationships with both AR and anemia as the exposure factors in a subsequent multivariate MR model of AR. The causal relationship between anemia and AR is expected to provide new therapeutic directions for future research on AR, offering an effective strategy for AR prevention.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1 Study reporting guidelines and study design\u003c/p\u003e\n\u003cp\u003eTwo-sample MR and public datasets were utilized to examine the impact of anemia indicators on AR. Study reporting adhered to the Strengthening the Reporting of Observational Studies in Epidemiology using MR (The STROBE-MR Statement) [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. A schematic of the study design is depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e2.2 Data sources\u003c/p\u003e\n\u003cp\u003eGenome-wide association study (GWAS) data for the anemia indicators GWAS IDs (finn-b-D3_ANAEMIANAS) were acquired from the MRC IEU OpenGWAS database, and standardized association summary statistics were generated using the R package TwoSampleMR. The study population comprised Finnish individuals, totaling 218,167 samples, including 5,929 cases of anemia and 212,238 controls.\u003c/p\u003e\n\u003cp\u003eGWAS data on AR were obtained, with the GWAS ID for AR (ukb-a-254) sourced from the MRC IEU OpenGWAS database. Standardized association summary statistics were generated using the R package TwoSampleMR. The study population consisted of Europeans, totaling 83,529 samples, with 18,934 cases of AR and 64,595 control participants.\u003c/p\u003e\n\u003cp\u003eWe initially identified two risk factors associated with AR from the literature [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]: smoking and air pollution. For smoking, we retrieved 58 smoker-related traits by searching the MRC IEU OpenGWAS database using \"smoke\" as the keyword (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Summary statistics of the associations for these 58 smoking indicators were obtained and standardized using the R package TwoSampleMR. Similarly, for air pollution, we obtained 50 smoking-related indicators by searching air pollution-related traits from the MRC IEU OpenGWAS database using \"Air pollution\" as the keyword (Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). The R package TwoSampleMR was used to obtain and standardize the association summary statistics of these 50 smoking indicators.\u003c/p\u003e\n\u003cp\u003e2.3 Instrumental variable selection\u003c/p\u003e\n\u003cp\u003eA valid instrumental variable for genetic variation must satisfy three core assumptions: (1) the hypothesis of association, meaning the selected instrumental variable must have a significant relationship with the exposure factor; (2) the independence assumption, ensuring the instrumental variable is not significantly associated with potential confounders that may influence both the exposure and outcome; (3) the exclusivity limitation, indicating that the instrumental variable can only influence the outcome through the pathway of \"instrumental variable \u0026rarr; exposure \u0026rarr; outcome.\"\u003c/p\u003e\n\u003cp\u003eIn this study, the instrumental variable screening criteria for exposure were as follows. Single nucleotide polymorphisms (SNPs) with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e in GWAS served as the primary screening criterion. SNPs in linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a physical distance of \u0026gt;\u0026thinsp;10,000 kb between each gene) were excluded. Instrumental variables from the GWAS of the outcome data were extracted based on the selected SNPS. F-statistics were calculated to assess weak instrumental bias. An F-statistic below 10 indicates weak instrument strength, potentially biasing the results [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, such variables were removed to mitigate result distortion. The F-statistic was calculated as follows:\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003cp\u003eF=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{N-k-1}{k}\\)\u003c/span\u003e\u003c/span\u003e\u0026times;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{R}^{2}}{1-{R}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ewhere n is the sample size, k is the number of instrumental variables used, and R\u003csup\u003e2\u003c/sup\u003e reflects the extent to which the instrumental variables explain exposure. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2 \u0026times; (1-MAF) \u0026times; MAF \u0026times; \u003csup\u003e2\u003c/sup\u003e\u0026beta;, where MAF is the minimum allele frequency and \u0026beta; is the allele effect size.\u003c/p\u003e\n\u003cp\u003e2.4 MR causal effect estimation\u003c/p\u003e\n\u003cp\u003eMultiple two-sample MR methods have been used to evaluate causal effects between exposures and outcomes, including inverse variance-weighted (IVW) [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], weighted median (WM) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], MR-Egger [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], weighted mode [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], and simple mode. Some studies have demonstrated [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] that the IVW method exhibits slightly greater robustness under specific conditions; it disregards the intercept term in regression and employs the inverse of the outcome variance as the weight for fitting. Therefore, in the absence of pleiotropy and with or without heterogeneity, the IVW method served as the primary MR analysis, supplemented by the other four methods (with the IVW random-effects model employed in the presence of heterogeneity). The MR-Egger method was utilized to estimate results in the presence of pleiotropy. Reverse causality was assessed for potential causal effects of exposure outcomes using the same methodology.\u003c/p\u003e\n\u003cp\u003e2.5 Sensitivity analysis\u003c/p\u003e\n\u003cp\u003eSensitivity analysis of the results was performed using the heterogeneity, pleiotropy, and one-by-one exclusion tests, as outlined below:\u003c/p\u003e\n\u003cp\u003e(1) Heterogeneity test: Cochran\u0026rsquo;s Q test was employed to assess heterogeneity among the SNP estimates. A statistically significant Cochran\u0026rsquo;s Q test indicated significant heterogeneity in the results. While Cochran\u0026rsquo;s Q test determined only the presence or absence of heterogeneity, it could not ascertain the distribution of heterogeneity. Therefore, the I\u003csup\u003e2\u003c/sup\u003e statistic was used to indicate the proportion of heterogeneous instrumental variables in the total variation: when I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;0, it was considered as 0, indicating no observed heterogeneity. I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0\u0026ndash;25%, mild heterogeneity; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;25\u0026ndash;50%, moderate heterogeneity; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%, high heterogeneity. The calculation formula is as follows:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Q-df}{Q}\\)\u003c/span\u003e\u003c/span\u003e\u0026times;100%\u003c/p\u003e\n\u003cp\u003e(2) Pleiotropy test: The MR-Egger method was used to examine the pleiotropy of instrumental variables. A P-value less than 0.05 for the MR-Egger's intercept indicated significant horizontal pleiotropy of genetic variation.\u003c/p\u003e\n\u003cp\u003e(3) Leave-one-out test: The MR results were recalculated by excluding individual SNPs one at a time to assess whether the SNP affected the association between anemia and AR. A substantial difference between the MR effect estimation and the total effect estimation following the exclusion of an instrumental variable indicated sensitivity of the MR effect estimation to that SNP.\u003c/p\u003e\n\u003cp\u003e2.6 Multivariate MR analysis\u003c/p\u003e\n\u003cp\u003eTo perform univariate MR analysis, all smoking and air pollution-related indicators retrieved from the MRC IEU OpenGWAS database were assessed against AR outcomes. A selection of IVW models with significant causal relationships was screened for presentation. Subsequently, the IVW model with significant causality from the initial screening was subjected to reverse Mendelian analysis. Finally, these significant findings were used in multivariate MR analysis to assess the direct effect of anemia on AR.\u003c/p\u003e\n\u003cp\u003e2.7 Statistical analysis\u003c/p\u003e\n\u003cp\u003eAll data calculations and statistical analyses were conducted using R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-projec\u003c/span\u003e\u003c/span\u003e t.org/, version 4.3.1). The TwoSampleMR package was primarily used for MR analysis [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Cochran\u0026rsquo;s Q test and leave-one-out analysis were used to evaluate the robustness and reliability of the results. A genetic pleiotropy test was performed using the MR-Egger intercept method. Evaluation metrics included odds ratios (OR) and 95% confidence intervals (95% CI). All statistical P-values were two-sided. For SNPs derived from GWAS studies, P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e was considered statistically significant. For other statistical tests, statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Analysis framework and flow chart\u003c/p\u003e\n\u003cp\u003e3.2 Instrumental variable screening\u003c/p\u003e\n\u003cp\u003eAccording to the screening criteria for instrumental variables in this study, SNPs with linkage disequilibrium were excluded. SNPs associated with to anemia indicators were included as instrumental variables after matching with the GWAS data of AR. The number of instrumental variables for anemia is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The MR analysis yielded significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the F-test statistics for the instrumental variables of anemia were all greater than 10. This indicated that the majority of the SNPs screened in this study were strong instrumental variables, and any potential bias due to weak instrumental variables was limited.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eScreening of instrumental variables for anemia and allergic rhinitis and F test results on the strength of instrumental variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of SNPs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian of F\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMinimum of F\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMaximum of F\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.92363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.79645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.91405\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSNPs, single nucleotide polymorphisms; F, F-statistics.\u003c/p\u003e\n\u003cp\u003e3.3 MR causal effect estimates\u003c/p\u003e\n\u003cp\u003eWe employed MR-Egger, weighted median, IVW, simple mode (SM) and weighted mode models for the analysis. Scatter plots of the SNP effect estimates are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. It is apparent that the fitting curves of the scatter plots for all five models exhibit consistent directions, with relatively consistent slopes observed across most models. Additionally, the intercept of the IVW model closely approximates 0.\u003c/p\u003e\n\u003cp\u003eThe estimation results of the causal effect of anemia on the incidence of AR are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The results of the IVW model indicate a significant causal association between anemia and AR. Specifically, more severe anemia was associated with a higher risk of AR (OR\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMendelian randomization causal effect estimates of anemia on the onset of allergic rhinitis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMethod\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ensnp\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of SNPs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBeta\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR95CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInverse variance weighted\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.020762\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.020762 (1.000173, 1.041775)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.048088\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.977368 (0.950099, 1.054156)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.977368\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimple mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.011596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.396149 (0.986261, 1.037583)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.396149\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.012172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.012172 (0.992176, 1.032572)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.234659\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.011596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.36243 (0.988047, 1.032572)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.36243\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e3.4 Sensitivity analysis\u003c/p\u003e\n\u003cp\u003eThe heterogeneity of the significant results was assessed using Cochran\u0026rsquo;s Q test and I\u003csup\u003e2\u003c/sup\u003e statistics, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The results showed moderate heterogeneity (Cochran\u0026rsquo;s Q p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 25% \u0026gt; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%) among significant anemia indicators in the MR results of AR (IVW). A funnel plot of the instrumental variable of anemia (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that the scatter points of the causal association effect were symmetrical, indicating no potential bias in the results. For indicators with highly heterogeneous results in the heterogeneity test, the random-effects model of IVW was used to estimate the causal effects, and the results are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMendelian randomization analysis heterogeneity test for the association between Anaemia and Allergic Rhinitis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emethod\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ df\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ pvalue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anaemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMR Egger\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.34993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.510701\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInverse variance weighted\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.70284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.038782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.491607\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eQ, Cochran's Q test statistic;Q df, degrees of freedom for the Q test; I\u003csup\u003e2\u003c/sup\u003e statistic reflects the proportion of heterogeneity attributed to instrumental variables in the total variability.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eInverse variance weighted random effects model analysis of the association between anemia and allergic rhinitis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of SNPs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBeta\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048088\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eSNPs: single nucleotide polymorphisms\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003eThe MR-Egger regression was used to test the horizontal pleiotropy of the instrumental variables. The statistical hypothesis test P-values of the intercept terms of each index exceeded 0.05, and the intercept was close to 0, indicating that the causal inference in this study was not influenced by horizontal pleiotropy (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab10\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMendelian randomization analysis of anemia\u0026rsquo;s impact on allergic rhinitis using a horizontal pleiotropy test\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMR-Egger intercept\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003878\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.439405\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSensitivity analysis of the results was conducted using the elimination test individually (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Each line in the figure represents the effect size and 95% confidence interval range of the index after the corresponding SNP is removed, with the red line representing the reference effect interval. Each line closely overlaps with the red-line interval, indicating that the effect estimate remains stable even with the removal of a single SNP. This suggests that the results are robust.\u003c/p\u003e\n\u003cp\u003eThe results of the reverse causality MR analysis showed no significant causal effect of AR on the anemia index (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e3.5 Multivariate MR analysis\u003c/p\u003e\n\u003cp\u003eWe conducted a univariate MR analysis of all smoking- and air pollution-related indicators retrieved from the MRC IEU OpenGWAS database and the outcomes of AR, respectively. Significant causal relationships were identified using a combination of IVW models (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The results indicated significant causal associations among smoking [cigarettes smoked per day (GWAS ID: ieu-b-142), cigarettes smoked per day (GWAS ID: ieu-b-4826)], type of tobacco previously smoked [manufactured cigarettes (GWAS ID: ukb-d-2877_1)], air pollution [particulate matter air pollution (pm10); 2010 (GWAS ID: ukb-e-24005_EAS)], and other indicators. A reverse Mendelian analysis was then performed on the combinations with significant causal relationships in the previously screened IVW model. The results of reverse causality MR analysis showed that AR had a significant effect on smoking [cigarettes smoked per day (GWAS ID: ieu-b-142), cigarettes smoked per day (GWAS ID: ieu-b-4826)], type of tobacco previously smoked [manufactured cigarettes (GWAS ID: ukb-d-2877_1)], and particulate matter air pollution [(pm10), 2010 (GWAS ID: ukb-e-24005_EAS)]. Other indicators had no causal effect (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the results are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab13\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCausal effect estimates of Mendelian randomization for smoke, air pollution, and allergic rhinitis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOutcome\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of SNPs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBeta\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR95CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCigarettes smoked per day (GWAS ID: ieu-b-142)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.984235 (0.971708, 0.996924)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015043\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCigarettes smoked per day (GWAS ID: ieu-b-4826)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003301\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.996704 (0.993863, 0.999552)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023366\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eType of tobacco previously smoked: manufactured cigarettes (GWAS ID: ukb-d-2877_1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.168094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.071974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.845274 (0.734061, 0.9733371)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019518\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParticulate matter air pollution (pm10); 2010 (GWAS ID: ukb-e-24005_EAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.014448 (1.004874, 1.024113)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSNP, single nucleotide polymorphism; \u0026beta;, the effect coefficients in Mendelian randomization analysis; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\n\u003ctable border=\"1\" width=\"539\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 7\u003c/p\u003e\n\u003cp\u003eReverse Mendelian randomization analysis of smoking (smoke) and air pollution (air pollution) on allergic rhinitis\u003c/p\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eOutcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eNumber of SNPs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eBeta\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eCigarettes smoked per day (GWAS ID: ieu-b-142)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCigarettes smoked per day\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.01349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.086104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.875502\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eCigarettes smoked per day (GWAS ID: ieu-b-4826)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eCigarettes smoked per day\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.00823\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.875349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.992498\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;Type of tobacco previously smoked: manufactured cigarettes (GWAS ID: ukb-d-2877_1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eType of tobacco previously smoked: manufactured cigarettes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.00463\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.017885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.795819\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eParticulate matter air pollution (pm10); 2010 (GWAS ID: ukb-e-24005_EAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.45725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.414422\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.269879\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSNP, single nucleotide polymorphism; \u0026beta;, the effect coefficients in the Mendelian randomization analysis.\u003c/p\u003e\n\u003cp\u003eFinally, a multivariate MR Analysis of AR was conducted separately using these significant results as exposures to assess the direct effect of anemia on AR (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Models 1, 2, and 3, adjusted for cigarettes smoked per day (GWAS ID: ieu-b-142), cigarettes smoked per day (GWAS ID: ieu-b-4826), type of tobacco previously smoked, and manufactured cigarettes (GWAS ID: ukb-d-2877_1), revealed no significant effect of anemia on AR. Model 4, adjusted for particulate matter air pollution [(pm10); 2010 (GWAS ID: ukb-e-24005_EAS)], indicated that anemia still exerted a significant direct effect on AR.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab16\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of multivariate Mendelian randomization analysis of the effects of smoking, air pollution, and anemia on the incidence of allergic rhinitis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOutcome\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBeta\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep- value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007825\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.191556\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCigarettes smoked per day (GWAS id: ieu-b-142)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018907\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002771\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.222482\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCigarettes smoked per day (GWAS id: ieu-b-4826)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001390\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008295\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.057864\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eType of tobacco previously smoked: manufactured cigarettes (GWAS id: ukb-d-2877_1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.120096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.199436\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther and unspecified anemias (GWAS id: finn-b-D3_ANAEMIANAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0302924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020361\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParticulate matter air pollution (pm10); 2010 (GWAS id: ukb-e-24005_EAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDoctor diagnosed hay fever or allergic rhinitis (GWAS id: ukb-a-254)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0137253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049762\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSNP, single nucleotide polymorphism; \u0026beta;, the effect coefficients in the multivariate Mendelian randomization analysis.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAR, a nasal mucosal allergic disease, manifests with symptoms such as nasal itching, congestion, sneezing, and clear mucus. Long-term persistence significantly affects patients\u0026rsquo; sense of smell, sleep, and overall quality of life. Despite ongoing research, the pathogenesis and underlying mechanisms of AR have not yet been fully elucidated.\u003c/p\u003e \u003cp\u003eThis study utilized datasets from the public database MRC IEU OpenGWAS to investigate the causal relationship between anemia and AR through two-sample MR. Various two-sample MR methods were used to evaluate exposure-outcome causal effects, and sensitivity, Cochran\u0026rsquo;s Q, and leave-one-out analyses were conducted to ensure result robustness. The MR Egger intercept method was used to conduct genetic pleiotropy testing, validating the reliability of the findings. In the IVW model, MR estimation revealed a significant causal relationship between anemia severity and AR onset. The more severe the anemia (OR\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the higher the AR risk. All smoking- and air pollution-related indicators retrieved from the MRC IEU OpenGWAS database were subjected to univariate MR analysis with significant causal relationships in the IVW model. The results showed that smoking and air pollution had significant causal relationships with AR. Subsequently, a reverse Mendelian analysis was performed on the combinations with significant causal relationships in the IVW model selected earlier. The results of the reverse causal MR analysis showed that AR had no causal effect on smoking or other indicators (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAnemia is a medical condition characterized by reduced levels of hemoglobin and a deficiency in red blood cell mass, which results in the inability to transport oxygen to peripheral tissues [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The two major subtypes of anemia are iron deficiency anemia and inflammatory anemia. Anemia is prevalent in children and may lead to impaired cognitive and motor development [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIron participates in energy metabolism, and, as it exists in many forms in the human body, its physiological functions are correspondingly extensive. For example, hemoglobin can transport oxygen, myoglobin can store oxygen, and cytochrome can transport electrons, combine with various enzymes to decompose peroxides, detoxify bacteria, and participate in the tricarboxylic acid cycle. Iron deficiency reduces the bactericidal ability of neutrophils and impairs lymphocyte function; however, iron supplementation can improve the immune function. The serum ferritin level and leukocyte bactericidal ability decreased, and peripheral blood T lymphocyte subset cell function was impaired. Elevated serum ferritin levels may promote an inflammatory response by mediating T lymphocyte immune imbalance, which leads to the occurrence and development of AR.\u003c/p\u003e \u003cp\u003eAccording to the findings of Japanese researchers, allergic diseases are related to caregiver-reported anemia in children. Considering the high risk of negative effects on growth/development and quality of life in children with anemia, increased clinician awareness regarding screening and monitoring of anemia may relieve the disease burden in children with allergic diseases. In this cohort analysis of the largest birth cohort in Japan, it was found that anemia at 3 years of age is more prevalent in children with caregiver-reported allergic conditions than in children without allergies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe etiology of the increased risk of anemia in children with allergic diseases remains unclear. Management of anemia in children with allergies is paramount, given its potential to impede growth and development. In addition to nutritional interventions for children with food allergies, increased clinical vigilance in the screening and monitoring of anemia in this population may alleviate the disease burden associated with both conditions.\u003c/p\u003e \u003cp\u003eCompared to traditional epidemiological studies, the advantage of this study is that it explored the causal relationship between anemia and AR using two-sample MR. This method uses randomly assigned genotypes as instrumental variables to infer the impact of exposure factors on outcomes, greatly reducing the impact of reverse causal relationships and confounding factors. Multiple MR analysis methods were used in this study to ensure the reliability and stability of the results. However, this study has certain limitations. First, the outcome indicator of AR was not further classified to explore the causal relationship between anemia and different types of AR. It should be included in the dataset of different types of AR for analysis to improve the accuracy of conclusions. Second, the summary data used in this study came from a European population, and the conclusions drawn lack universality. Therefore, data from different ethnic groups should be included for supplementary verification. Finally, this study only explored the relationship between anemia and AR from a genetic perspective, but its specific mechanism of action needs further research and verification.\u003c/p\u003e \u003cp\u003eOur study confirmed a significant causal relationship between anemia and AR. This suggests that anemia is a risk factor for AR. Elevated serum ferritin levels may promote an inflammatory response by mediating T lymphocyte immune imbalance, which leads to the occurrence and development of AR. How this affects future research on AR and the specific action mechanism still needs to be tested. Our results provide a platform for researchers to explore the relationship between anemia and AR. Future studies should elucidate the mechanism underlying the association between anemia and AR, or explore novel treatment modalities to prevent or treat AR.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: All authors agree to submit their papers to the journal for publication. We appreciate Han XinMin for support and assistance in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was funded by Shanghai Baoshan District Medical Key Specialty Class B: Community Integrated Chinese and Western Medicine Paediatrics Construction(BSZK-2023-BZ09 )\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e：All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chen YanNi, Song ChenFei and Yu haidong. The first draft of the manuscript was written by Liu Tao .Han XinMin approved the final manuscript. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e: The data sources employed in our research were obtained from publicly available databases; therefore, securing approval from an ethics committee was considered unnecessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e：This article contains no studies with human or animal subjects performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e: Chen YanNi, Song ChenFei, Yu HaiDong, Liu Tao, Han XinMin all declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eCalderon MA, Carr VA, Jacobson M, Sheikh A, Durham S. Allergen injection immunotherapy for perennial allergic rhinitis. \u003cem\u003eCochrane Database Syst Rev\u003c/em\u003e. 2019;2019(1):CD007163. Published 2019 Jan 8. doi:10.1002/14651858.CD007163.pub2\u003c/li\u003e\n\u003cli\u003eSchwarz C, Eschenhagen P, Schmidt H, et al. Antigen specificity and cross-reactivity drive functionally diverse anti-Aspergillus fumigatus T cell responses in cystic fibrosis. \u003cem\u003eJ Clin Invest\u003c/em\u003e. 2023;133(5):e161593. Published 2023 Mar 1. doi:10.1172/JCI161593\u003c/li\u003e\n\u003cli\u003eCalderon MA, Carr VA, Jacobson M, Sheikh A, Durham S. Allergen injection immunotherapy for perennial allergic rhinitis. \u003cem\u003eCochrane Database Syst Rev\u003c/em\u003e. 2019;2019(1):CD007163. Published 2019 Jan 8. doi:10.1002/14651858.CD007163.pub2\u003c/li\u003e\n\u003cli\u003eBarca-Hernando M, Mu\u0026ntilde;oz-Martin AJ, Rios-Herranz E, et al. Case-Control Analysis of the Impact of Anemia on Quality of Life in Patients with Cancer: A Qca Study Analysis. \u003cem\u003eCancers (Basel)\u003c/em\u003e. 2021;13(11):2517. Published 2021 May 21. doi:10.3390/cancers13112517\u003c/li\u003e\n\u003cli\u003eYang L, Sato M, Saito-Abe M, et al. Allergic Disorders and Risk of Anemia in Japanese Children: Findings from the Japan Environment and Children\u0026apos;s Study. \u003cem\u003eNutrients\u003c/em\u003e. 2022;14(20):4335. Published 2022 Oct 17. doi:10.3390/nu14204335\u003c/li\u003e\n\u003cli\u003eSkrivankova, V.W., et al., Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. Jama, 2021. 326(16): p. 1614-1621.\u003c/li\u003e\n\u003cli\u003ePang, K., et al., Prevalence and Risk Factors for Allergic Rhinitis in China: A Systematic Review and Meta-Analysis. Evid Based Complement Alternat Med, 2022. 2022: p. 7165627.\u003c/li\u003e\n\u003cli\u003eEguiluz-Gracia, I., et al., The need for clean air: The way air pollution and climate change affect allergic rhinitis and asthma. Allergy, 2020. 75(9): p. 2170-2184.\u003c/li\u003e\n\u003cli\u003ePierce, B.L., H. Ahsan, and T.J. Vanderweele, Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol, 2011. 40(3): p. 740-52.\u003c/li\u003e\n\u003cli\u003eBurgess, S., A. Butterworth, and S.G. Thompson, Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol, 2013. 37(7): p. 658-65.\u003c/li\u003e\n\u003cli\u003eBowden, J., et al., Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol, 2016. 40(4): p. 304-14.\u003c/li\u003e\n\u003cli\u003eBowden, J., G. Davey Smith, and S. Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol, 2015. 44(2): p. 512-25.\u003c/li\u003e\n\u003cli\u003eHartwig, F.P., G. Davey Smith, and J. Bowden, Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol, 2017. 46(6): p. 1985-1998.\u003c/li\u003e\n\u003cli\u003eHemani, G., K. Tilling, and G. Davey Smith, Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet, 2017. 13(11): p. e1007081.\u003c/li\u003e\n\u003cli\u003eYang L, Sato M, Saito-Abe M, et al. Allergic Disorders and Risk of Anemia in Japanese Children: Findings from the Japan Environment and Children\u0026apos;s Study. Nutrients. 2022;14(20):4335. Published 2022 Oct 17. doi:10.3390/nu14204335\u003c/li\u003e\n\u003cli\u003eMeena, K.; Tayal, D.K.; Gupta, V.; Fatima, A. Using classification techniques for statistical analysis of Anemia. Artif. Intell. Med.2019, 94, 138\u0026ndash;152.\u003c/li\u003e\n\u003cli\u003eRodak, B.F.; Keohane, E.M.; Fritsma, G.A. Hematology-E-Book: Clinical Principles and Applications; Elsevier Health Sciences:Amsterdam, The Netherlands, 2013.\u003c/li\u003e\n\u003cli\u003eIglesias Vazquez, L.; Valera, E.; Villalobos, M.; Tous, M.; Arija, V. Prevalence of Anemia in Children from Latin America andthe Caribbean and Effectiveness of Nutritional Interventions: Systematic Review and Meta(-)Analysis. Nutrients 2019, 11, 183.\u003c/li\u003e\n\u003cli\u003eStevens, G.A.; Finucane, M.M.; De-Regil, L.M.; Paciorek, C.J.; Flaxman, S.R.; Branca, F.; Pena-Rosas, J.P.; Bhutta, Z.A.; Ezzati,M.; Nutrition Impact Model Study, G. Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995\u0026ndash;2011: A systematic analysis of populationrepresentative data. Lancet Glob. Health 2013, 1, e16\u0026ndash;e25.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"anemia, allergic rhinitis, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4244796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4244796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnemia and allergic rhinitis (AR) are prevalent clinical conditions in children. Previous studies have suggested an association between anemia and AR. Using Mendelian randomization (MR) analysis, we investigated causality with regard to the influence of anemia indicators on the incidence of AR.\u003c/p\u003e \u003cp\u003eWe searched the IEU OpenGWAS database for summary information on genome-wide association studies (GWAS) of anemia and AR in European populations. Three methods, MR-Egger, weighted median (WM), and inverse variance weighted (IVW), were utilized to evaluate the causal link between SLE and thyroid disease. We assessed pleiotropy and heterogeneity using MR-Egger intercept, MR-PRESSO, and Cochran\u0026rsquo;s Q test, among others.\u003c/p\u003e \u003cp\u003eThe IVW model revealed a significant causal association between anemia and AR. As the severity of anemia increased (OR\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the risk of AR also increased. Moderate heterogeneity was observed (Cochran\u0026rsquo;s Q p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 25% \u0026gt; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%) among significant anemia-associated indicators in the MR results of AR (IVW). The MR-Egger regression was used to assess the horizontal pleiotropy of the instrumental variables. The statistical hypothesis test P-values of the intercept terms of each index exceeded 0.05, and the intercept was close to zero, indicating that the causal inference in this study was unaffected by horizontal pleiotropy.\u003c/p\u003e \u003cp\u003eThe MR analysis results support a potential causal link between anemia and AR, suggesting that anemia is a risk factor for AR. These insights could contribute towards raising awareness regarding the pathogenesis of AR and aid the formulation of strategies for its prevention, treatment, and prognosis.\u003c/p\u003e","manuscriptTitle":"Causal inference study on the influence of anemia indicators on the incidence of allergic rhinitis: two- sample Mendelian randomization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 19:00:06","doi":"10.21203/rs.3.rs-4244796/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eeb4dee6-aefb-4997-8410-b1bf8bb070db","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-03T03:24:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 19:00:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4244796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4244796","identity":"rs-4244796","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}