Metabolic Biomarkers Mediate Allergic Conjunctivitis via Circulating Inflammatory Proteins: Evidence From a Mendelian Randomization Study

Abstract

Purpose: This study aimed to investigate the mediating role of metabolic biomarkers (MBs) in the association between circulating inflammatory proteins (CIPs) and allergic conjunctivitis (AC) and identify potential therapeutic targets.

Methods: A Mendelian randomization (MR) study was conducted, leveraging genetic variants as instrumental variables to infer causal relationships. Data were obtained from genome-wide association studies (GWASs), and a two-sample MR was employed to estimate the direct and indirect effects of CIPs on AC through MBs. Inverse-variance weighting (IVW) served as the primary analysis method, supplemented by sensitivity analyses to assess the robustness of findings.

Results: Six CIPs were found to have significant causal effects on AC. Natural killer cell receptor 2B4 (CD244) exhibited a protective effect, and interleukin-18 receptor 1 (IL-18R1), IL-6, leukemia inhibitory factor (LIF), protein S100-A12 (EN-RAGE), and TNF-related activation-induced cytokine (TRANCE) were identified as risk factors. The MR analysis indicated the mediation role of specific MBs in these associations, with MBs such as 4-oxo-retinoic acid, gulonate, 3-(4-hydroxyphenyl) lactate, S-adenosylhomocysteine, and sphingomyelin, significantly influencing the pathway from CIPs to AC.

Conclusions: This study highlights the crucial role of MBs in mediating the association between CIPs and AC. These findings offer new insights into the pathophysiology of AC and suggest potential metabolic targets for novel therapeutic approaches.

Translational Relevance: This study underscores the potential for targeting specific MBs as novel therapeutic approaches to modulate the inflammatory pathways implicated in AC.

Introduction

Allergic conjunctivitis (AC) is a common immune-mediated hypersensitivity disorder characterized by ocular symptoms such as itching, redness, and tearing.^1–3 These symptoms result from a complex immune response, where environmental allergens such as pollen or dust mites trigger an inflammatory cascade in genetically predisposed individuals.^4,5 The pathophysiology of AC involves interactions between genetic and environmental factors, leading to inflammation in the conjunctiva and subsequent tissue damage.^6–8 In addition to these ocular manifestations, AC often results in fatigue and difficulty concentrating, which can severely impact an individual's quality of life and daily activities.⁹ AC remains a significant public health challenge, severely diminishing quality of life and imposing a substantial economic burden on society due to direct medical costs and lost productivity.^3,6,10 Although current treatments, such as antihistamines and corticosteroids, are effective in alleviating symptoms, they do not address the underlying mechanisms of the disease.^1,2 This gap in therapeutic options underscores the need for a deeper understanding of the molecular processes involved, which could lead to the development of more targeted and effective therapies. 

Recent research has highlighted the crucial roles of circulating inflammatory proteins (CIPs) and metabolic biomarkers (MBs) in allergic diseases.^4,8,11 CIPs play a key role in regulating immune and inflammatory responses, with their levels often altered in various diseases, which is highly valuable in clinical medicine.¹² MBs serve as the footprints of biological processes and can function as signaling mediators and immunomodulators, significantly impacting the pathophysiology of diseases.^7,13 Moreover, serum metabolomics provides insights into the complex effects of diseases on individuals, helping to identify metabolic changes that occur in response to inflammatory processes.¹⁴ Studies have demonstrated that IL-33 is a key inflammatory mediator, playing a central role in modulating immune responses by inducing T helper 2 (Th2) cell immune responses in refractory allergic diseases.^4,8 Similarly, omega-3 fatty acids and their MBs (e.g., resolvins, protectins) have shown significant anti-inflammatory properties in combating allergic diseases.¹⁵ These MBs, rather than the omega-3 fatty acids themselves, mediate the anti-inflammatory effects, and their levels can be measured in the blood. However, the precise mechanisms by which these biomarkers influence the onset and progression of AC remain unclear. Understanding these causal relationships in AC could lead to new therapeutic targets that are more effective and specific than current treatments. 

Traditional observational studies often struggle to establish causal links between CIPs and allergic diseases due to biases and reverse causality.¹⁶ Biases arise from confounding factors such as environmental exposures, lifestyle choices, and comorbidities that can influence both CIP levels and disease risk.^16,17 Reverse causality occurs when the disease itself alters CIP levels, making it challenging to discern cause from effect. In AC research, these challenges are intensified by diverse environmental and genetic factors.^7,18 Environmental influences include exposure to allergens such as pollen, dust mites, and pet dander, as well as air pollution and climate variations.^1,6 Genetic predispositions, particularly variations in genes regulating immune responses, further complicate the landscape.⁵ The interplay of these factors introduces significant variability, hindering the isolation of specific causal relationships. Mendelian randomization (MR) offers a solution by using genetic variants as instrumental variables to infer causality.¹⁹ Because genetic variants are randomly assigned at conception, they are generally free from the confounding factors and reverse causation that plague observational studies.^17,19 This method enhances the robustness of causal inferences, providing clearer insights into disease etiology.¹⁷ For example, a study by Zhou et al.²⁰ utilized MR to establish a causal link between atopic dermatitis and AC, demonstrating the utility of this approach in identifying causal relationships. Understanding these causal pathways is crucial for developing effective prevention and treatment strategies for AC. By minimizing biases inherent in traditional studies, MR enables a more accurate assessment of the role of CIPs in allergic diseases, paving the way for innovative therapeutic interventions.¹⁸ In this study, we pioneered the use of a mediated MR approach to explore the roles of 1400 MBs in the association of 91 CIPs with AC. By integrating genome-wide association study (GWAS) data and relevant biomarkers, we aimed to illuminate the intricate connections between genetic susceptibility, inflammatory responses, and allergic diseases. Our findings promise to deepen our understanding of this prevalent allergic condition, ultimately guiding the development of targeted and effective interventions. 

Methods

Study Design

In this study, a two-step MR was employed to investigate the mediating role of MBs in the relationship between CIPs and the risk of AC. Single-nucleotide polymorphisms (SNPs) were defined as instrumental variables (IVs). MR studies are based on three core assumptions: (1) IVs are strongly associated with exposure; (2) IVs are unrelated to confounding factors; and (3) IVs can only affect the outcome through exposure.¹⁷ The design of this study is illustrated in Figure 1. 

Figure 1.

 

Schematic diagram of this study. (A) The total effect c was analyzed using genetically predicted CIPs as the exposure and AC as the outcome. Additionally, the total effect d was also analyzed using genetically predicted AC as the exposure and CIPs as the outcome. The relevance and independence of IVs were considered for both exposure and outcome, and potential confounders were accounted for. (B) The total effect was further broken down as follows: The indirect effect was assessed using a two-step approach involving the effects of CIPs on MBs (a) and the effects of MBs on AC (b), as well as the product method (a × b), and the direct effect was calculated as c′ = c – a × b. The proportion mediated was determined by dividing the indirect effect by the total effect.

Data Source

The GWAS summary statistics used in this study were publicly available and involved secondary analysis of previously published data, thereby exempting the need for ethical approval. Genetic associations for AC were obtained from the FinnGen consortium, which includes 23,665 cases and 388,516 controls, primarily of European descent. Further details about this dataset can be accessed at https://www.finngen.fi/en/access_results. The FinnGen project was selected due to its large sample size, data diversity, and rigorous quality control standards, making it an ideal resource for GWAS summary data on AC. Ninety-one CIPs were derived from the GWAS summary data of 14,824 individuals of European ancestry.²¹ This dataset was chosen for its comprehensive coverage of inflammatory proteins and their genetic associations. Complete GWAS summary statistics for these proteins are available at https://www.phpc.cam.ac.uk/ceu/proteins. 

The plasma metabolome data were sourced from the Canadian Longitudinal Study of Aging cohort, which included 8299 participants.²² This dataset offers a broad analysis of plasma metabolite levels. The summary data for 1400 MBs included 1091 blood metabolites and 309 metabolite ratios, obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas). The dataset was selected for its comprehensive coverage of metabolic biomarkers relevant to allergic diseases and inflammation.⁷ 

Selection of Genetic Instruments

To meet the correlation criteria and obtain the required number of SNPs for analysis, the included SNPs had to be genome-wide significant (P < 1 × 10^–5).¹¹ SNPs with r² < 0.001 were selected to ensure independence, with a distance greater than 10,000 kb between each pair of SNPs to avoid linkage disequilibrium due to physical proximity.²³ In the reverse MR analysis, SNPs in the AC summary data were selected with a threshold of P < 5 × 10^–8, and linkage disequilibrium was eliminated using the same method (kb = 10,000, r² < 0.001).¹⁷ Palindromic SNPs with the same allele on the forward and reverse strands were excluded due to ambiguity in determining the strand of the effect allele.²⁴ Weak IVs were excluded by calculating the F statistic, F = R²(n – k – 1)/k(1-R²), with weak IVs (F < 10) being removed.²⁵ 

Statistical and Sensitivity Analysis

A schematic diagram of the study analysis is presented in Figure 1. Initially, bidirectional MR analysis was conducted to assess the potential causal relationship between SNPs associated with 91 CIPs and AC, determining the total effect (c). Subsequently, the two-step MR analysis was performed to investigate the mediating role of MBs. The first step involved examining the effects of CIPs on MBs, followed by assessing the impact of MBs on AC. The indirect effect (a × b) was calculated as the product of these estimates, and the direct effect (c′) was derived by subtracting the indirect effect from the total effect (c – a × b).²⁶ 

Five MR analysis methods were employed, including inverse variance weighting (IVW), Mendelian randomization–Egger (MR-Egger), weighted median method, simple model, and weighted model method.¹⁷ Results primarily relied on IVW, with statistical significance defined as P < 0.05.²⁶ Heterogeneity was evaluated using the Cochran Q test, with P < 0.05 as the threshold for heterogeneity.²⁷ The IVW random-effects model was utilized in this scenario, and the fixed-effects model was employed otherwise.²⁸ To enhance robustness, the MR-Egger method and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method were used to identify horizontal pleiotropy.^29,30 MR-Egger regression assessed horizontal pleiotropy of SNPs acting as IVs through the deviation from zero of the intercept term, with a non-zero intercept indicating bias in the IVW estimate.³¹ The MR-PRESSO outlier test was employed to eliminate anomalous SNPs and calculate corrected results to further mitigate horizontal pleiotropy.³⁰ Furthermore, leave-one-out analysis was conducted to exclude outlier SNPs and prevent a single SNP from unduly influencing the overall causal estimate. 

These findings were visually depicted through forest plots, funnel plots, scatterplots, and leave-one-out plots. All statistical analyses were carried out using the TwoSampleMR 0.5.6 and MRPRESSO 1.0 packages within R 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). 

Results

Detailed Information on the Included SNPs of the Analysis

According to the selection criteria for IVs (Fig. 2), a total of 2945 SNPs were selected as IVs for 91 CIPs and 34,843 SNPs were selected as IVs for 1400 MBs (Supplementary Tables S1, S2). 

Figure 2.

 

Flowchart of data source and SNP selection process for exposure (CIPs), mediator (MBs), and outcome (AC). Linkage disequilibrium, LD.

Causal Effects Between CIPs and AC

To obtain the total effect (c), the association between CIPs and AC was investigated using a two-sample MR analysis. As shown in Figures 3A and 3B, the IVW method was employed as the primary MR analysis method, which identified six CIPs with a causal relationship with AC. Among them, natural killer cell receptor 2B4 (CD244) (odds ratio [OR] = 0.928; 95% confidence interval [CI], 0.866–0.994; P = 0.0331) had a protective effect, and tumor necrosis factor–related activation-induced cytokine (TRANCE) (OR = 1.045; 95% CI, 1.002–1.089; P = 0.0386), protein S100-A12 (EN-RACE) (OR = 1.076; 95% CI, 1.015–1.089; P = 0.0140), interleukin-18 receptor 1 (IL-18R1) (OR = 1.046; 95% CI, 1.013–1.079; P = 0.0052), leukemia inhibitory factor (LIF) (OR = 1.078; 95% CI, 1.013–1.148; P = 0.0184), and interleukin 6 (IL-6) (OR = 1.129; 95% CI, 1.001–1.273; P = 0.0473) were risk factors. The causal effects were visualized using circular heatmaps (Figs. 3A, 3B). Comprehensive insights into the causal relationship were provided through the scatterplots shown in Figures 3C to 3H and Supplementary Figure S1, with sensitivity analyses showing no substantial heterogeneity and pleiotropy (Supplementary Table S3). Further MR results are detailed in Supplementary Table S4. The reverse MR analysis showed no significant results (Figs. 3I, 3J). 

Figure 3.

 

Bidirectional MR analysis between CIPs and AC. (A) The circular heatmap of P values illustrates the causal relationship between CIPs as exposures and AC as the outcome using various MR methods. The vertical axis represents six CIPs (TRANCE, LIF, IL-6, IL-18R1, EN-RAGE, and CD244), and the horizontal axis, from outermost to innermost, represents five MR methods (weighted mode, simple mode, IVW, weighted median, and MR Egger). Lower P values provide stronger evidence for a causal relationship; the IVW method identified six CIPs with significant causal associations with AC. (B) The circular heatmap of OR visualizes the strength and direction of causal associations between CIPs as exposures and AC as the outcome. Six CIPs showed significant associations, with OR analysis identifying both protective and risk factors. TRANCE, LIF, IL-6, IL-18R1, and EN-RAGE were risk factors, and CD244 was a protective factor. (C–H) Scatterplots of causal effects of SNPs associated with TRANCE (C), EN-RAGE (D), IL-18R1 (E), CD244 (F), LIF (G), and IL-6 (H) on AC. These plots demonstrate individual causal relationships and provide visual evidence of consistency across different SNPs. (I) The circular heatmap of P values displays the results of reverse MR analysis with AC as the exposure and CIPs as the outcome. No significant results were observed, indicating the absence of a reverse causal relationship from AC to the six CIPs. (J) Schematic representation of the bidirectional MR analysis results that summarizes the significant causal relationships identified and highlights the lack of significant reverse associations.

Causal Effects of CIPs on MBs

To obtain the results of the β₁ effect (a), the causal effect of CIPs on MBs was analyzed. The two-sample MR analysis demonstrated significant causal effects of CIPs on several MBs. Six CIPs associated with AC were included in the mediation MR analysis. IVW analysis found IL-18R1 protective for 4-oxo-retinoic acid (OR = 0.933; 95% CI, 0.887–0.981; P = 0.0068) but a risk factor for gulonate (OR = 1.068; 95% CI, 1.004–1.136; P = 0.0376). TRANCE increased levels of 3-(4-hydroxyphenyl) lactate (OR = 1.068; 95% CI, 1.004–1.136; P = 0.0376), and protein S100-A12 (EN-RAGE) increased S-adenosylhomocysteine (SAH) levels (OR = 1.130; 95% CI, 1.021–1.251; P = 0.0178). CD244 decreases led to lower sphingomyelin (SM) levels (OR = 0.912; 95% CI, 0.844–0.986; P = 0.0213). These findings are visualized in Supplementary Figure S2 and were confirmed by sensitivity analyses (Supplementary Table S5). Detailed MR results are provided in Supplementary Table S6. 

Causal Effects of MBs on AC

To obtain the results of the β₂ effect (b), the causal effect of MBs on the risk of AC was analyzed. The analysis revealed significant causal effects of certain MBs on the risk of AC. Specifically, increased levels of 3-(4-hydroxyphenyl) lactate (OR = 1.058; 95% CI, 1.003–1.115; P = 0.0375), SM (OR = 1.069; 95% CI, 1.007–1.134; P = 0.0278), SAH (OR = 1.053; 95% CI, 1.007–1.100; P = 0.0231), and gulonate (OR = 1.066; 95% CI, 1.004–1.133; P = 0.0374) were linked to higher AC risk, but 4-oxo-retinoic acid was protective (OR = 0.920; 95% CI, 0.849–0.998; P = 0.0444) (Fig. 4). These relationships were robust in sensitivity analyses (Supplementary Table S7); visualizations are shown in Supplementary Figure S3, and further details are provided in Supplementary Table S8. 

Figure 4.

 

Forest plot to visualize the causal effects of MBs with CIPs and AC.

Genetically Predicted MBs Mediate the Association of CIPs With AC

After identifying the effect of exposure on the mediator and the significant mediators affecting AC, the mediated effect proportion and direct effect were quantified. The effects of IL-18R1 on AC were partially mediated by 4-oxo-retinoic acid (mediated proportion = 12.9%; mediated effect = 0.0058) and gulonate (8.36%; –0.0113). The effects of TRANCE were mediated by 3-(4-hydroxyphenyl) lactate (8.36%; 0.0037), EN-RAGE by SAH (8.52%; 0.0063), and CD244 by SM (8.1%; –0.0061) (Supplementary Table S9). Negative mediating roles were noted for gulonate and SM, whereas others had positive mediating roles (Fig. 4). These results indicate that changes in MBs played a significant role in the pathway from CIPs to AC. 

Discussion

The investigation into the roles of CIPs and MBs in AC is crucial for understanding the underlying pathophysiology of this condition.^7,32 Allergic diseases are influenced by complex interactions between genetic and environmental factors, with inflammatory proteins and metabolic processes playing significant roles.^3,33 In this study, the roles of 91 CIPs and 1400 MBs in the development of AC were initially investigated using the mediation MR approach. Six CIPs were identified to have significant causal effects on AC. CD244 exhibited a protective effect, whereas IL-18R1, LIF, IL-6, EN-RAGE, and TRANCE were identified as risk factors. These findings underscore the critical roles of specific CIPs in the pathogenesis of AC. 

Previous studies have demonstrated that inflammatory cytokines are extensively involved in immune and inflammatory responses.^34–36 Multiple studies have shown that the application of biologics targeting IL-5, IL-4, and IL-13 antibodies significantly improves the treatment of allergic diseases including asthma, rhinitis, and atopic dermatitis; however, these drugs have not been approved for use in ocular allergic diseases.³⁷ IL-18 and IL-6 function as pro-inflammatory cytokines. IL-18 induces various leukocytes and participates in allergic reactions in conditions such as rhinitis, dermatitis, and asthma.³⁵ IL-18R1, the receptor for IL-18, is essential for mediating IL-18 signaling.³⁸ Inhibiting the binding of IL-18 to IL-18R1 may decrease the risk of inflammatory diseases.³⁸ Research has demonstrated that elevated levels of IL-18R1 are associated with an increased risk of allergies, hay fever, and eczema.³⁹ IL-6 exacerbates allergic reactions by promoting the production of Th2-type cytokines.³⁴ Tao et al.⁴⁰ found that blocking Th2 signaling can alleviate the inflammatory response and clinical symptoms of allergic conjunctivitis. Moreover, LIF is a pleiotropic cytokine within the IL-6 superfamily.⁴¹ Our results support the notion that elevated levels of IL-18R1, IL-6, and LIF are associated with an increased risk of AC. The identification of these inflammatory cytokines as risk factors for AC highlights their potential as therapeutic targets. Targeting IL-18R1 and IL-6 could modulate the inflammatory cascade, potentially leading to new treatments for AC. 

CD244 is a signaling molecule expressed on various immune cells, including natural killer (NK) cells, T cells, monocytes, eosinophils, and mast cells, where it plays a crucial role in immune regulation.⁴² In CD244^–/– mice with mild atopic dermatitis (AD), moderate eosinophil infiltration in the skin suggested that CD244 enhances eosinophil trafficking.⁴³ Furthermore, in a chronic AD model, CD244^–/– mice exhibited excessive mast cell degranulation, highlighting the inhibitory role of CD244 in mast cell activation.⁴³ CD244 also participates in eosinophil adhesion and chemotaxis, as its expression increased after the onset of allergic rhinitis (AR).⁴⁴ This upregulation in AR, a condition closely related to AC, suggests that CD244 may facilitate the recruitment and activation of eosinophils at sites of allergic inflammation. Given the pivotal role of eosinophils in the inflammatory processes of AC, regulation of their trafficking and activation by CD244 could significantly influence the severity of AC. Our findings indicate that CD244 may act as a protective factor against AC. Although its relationship with AC has not yet been studied, it is hypothesized that this protective effect may result from the ability of CD244 to modulate eosinophil function and mast cell activation, which are key drivers of the inflammatory cascade in AC.^4,6 

EN-RAGE, a member of the S100 protein family, binds to and activates RAGE signal transduction and is released by various cells under inflammatory conditions, participating in multiple inflammatory responses.^45,46 S100A8/9 is highly upregulated in inflammatory bowel disease and can serve as a biomarker of intestinal inflammation due to its high stability in fecal samples.⁴⁷ Similarly, EN-RAGE is significantly elevated in patients with chronic rhinosinusitis.⁴⁸ Genetic prediction revealed that elevated EN-RAGE is associated with an increased risk of AC. Although MR analysis suggested that TRANCE was risk factors for allergic conjunctivitis, there is insufficient research supporting their roles in other allergic diseases. The protective effect of CD244 may be related to its role in regulating immune responses, whereas the risk effects of IL-18, IL-6, LIF, EN-RAGE, and TRANCE may be related to their roles in promoting inflammatory responses. In addition, AC itself may influence changes in CIPs. Therefore, we investigated the reverse causality between AC and CIPs and found no significant results. 

MBs, as small-molecule products of metabolic reactions, are influenced by various factors such as genetics and diseases, and they also impact disease risk.^22,49 Alterations in the levels of CIPs in the circulatory system may impact metabolic processes, resulting in changes in the levels of MBs.⁵⁰ Our results highlighted the mediating role of specific metabolites in these associations, with metabolites such as 4-oxo-retinoic acid, gulonate, 3-(4-hydroxyphenyl) lactate, SAH, and SM significantly influencing the pathway from CIPs to AC. Specifically, the effect of IL-18R1 on AC was partially mediated by 4-oxo-retinoic acid and gulonate, TRANCE by 3-(4-hydroxyphenyl) lactate, EN-RAGE by SAH, and CD244 by SM. These findings suggest that metabolites play a crucial role in mediating the effects of CIPs on AC. Therefore, interventions aimed at modulating metabolite levels may represent a novel therapeutic approach. 

Previous studies have demonstrated that these MBs are associated with various diseases and play critical roles in regulating immune and inflammatory responses. For example, 4-oxo-retinoic acid, a bioactive metabolite of vitamin A, primarily exerts its effects through the retinoic acid receptor pathway.^51,52 It modulates immune responses by regulating the balance of T helper (Th) cells, specifically promoting regulatory T (Treg) cell differentiation and inhibiting Th2/Th17 responses.⁵² In the context of allergic inflammation, 4-oxo-retinoic acid may help maintain immune tolerance and reduce inflammation severity by regulating key immune pathways.^51,52 Similarly, gulonate, an intermediate product of glucose metabolism, participates in inflammatory processes.⁵³ Recent studies have shown that gulonate increases the risk of age-related macular degeneration, a condition marked by immune dysregulation and inflammation.⁵⁴ This study suggests that gulonate may contribute to ocular inflammation by modulating immune pathways, potentially influencing conditions such as AC. Furthermore, IL-18, a cytokine involved in immune responses, may modulate AC by regulating the levels of both 4-oxo-retinoic acid and gulonate, highlighting the complex interplay of metabolic pathways in ocular inflammation. 

Another MB, 3-(4-hydroxyphenyl) lactate, a product of tyrosine metabolism, is linked to the interaction between intestinal microbiota and host metabolism.⁵⁵ This interaction not only influences metabolic disorders such as type 2 diabetes but also promotes systemic inflammation, which could contribute to AC pathogenesis.^55,56 Additionally, TRANCE, a cytokine, has been shown to increase the risk of AC by promoting the accumulation of 3-(4-hydroxyphenyl) lactate, potentially exacerbating ocular inflammation. SAH, an intermediate in the methionine cycle, accumulates in response to oxidative stress and inflammation, both of which are critical to AC development.^1,57,58 Elevated SAH levels are associated with enhanced inflammatory responses in the eye, with EN-RAGE promoting its accumulation and increasing AC risk. Furthermore, SM, an essential component of cell membranes involved in cell signaling, has been implicated in regulating inflammatory diseases.⁵⁹ In the context of AC, alterations in SM metabolism may influence immune cell activation and ocular inflammation. CD244 may mitigate AC by reducing SM levels, thereby limiting inflammatory processes within the eye. 

By leveraging MR to infer causal relationships, the influence of confounding factors was reduced.¹⁷ The use of GWAS data enhanced the statistical power and robustness of our findings.²⁶ Despite our stringent selection criteria, SNP selection may have inherent limitations. Measurement variability of MBs and CIPs could have introduced bias, and potential unmeasured confounders could have influenced our results. Additionally, potential false-positive results due to multiple hypothesis testing were not corrected by the false discovery rate. Although MR analysis provides evidence of causality, a combination of biological mechanism studies and clinical data is needed to fully understand its role. 

In conclusion, this study reveals significant causal relationships between CIPs and AC, with MBs playing mediating roles. These findings offer new perspectives on the pathophysiology of AC and suggest potential directions for future research and clinical applications. Further exploration of these pathways could inform the development of targeted interventions to improve the management and prevention of AC. 

Acknowledgments

The authors thank the investigators of the original studies for sharing the GWAS summary statistics. 

Supported by grants from the National Natural Science Foundation of China (82371033, 32200684), Tianjin Health Research Project (TJWJ2022QN078), and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-016A). 

Author Contributions: ZJ and XY were responsible for the conception and design of the study. XC, ZX, QL, and BZ were responsible for the acquisition and analysis of data. XC and ZX wrote the paper. ZJ and XY reviewed the paper. All authors read and approved the final manuscript for submission. 

Disclosure: X. Cao, None; Z. Xu, None; B. Zhang, None; Q. Li, None; Z. Jiang, None; X. Yuan, None