Lung Function as a Biomarker for Glaucoma: The UK Biobank Study

Abstract

Purpose: To investigate the associations of lung function with glaucoma and related traits, explore the interactions between glaucoma genetic risk and lung function, and assess the causal relationships using Mendelian randomization (MR).

Methods: This cross-sectional study involved 85,369 participants with lung function measurements at baseline from the UK Biobank. Associations between lung function parameters and glaucoma and related traits were tested by multivariable logistic and linear regression. Two-sample MR analyses were conducted using summary statistics from large genetic datasets.

Results: Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), and FEV₁/FVC ratio were inversely associated with glaucoma, with the lowest quartiles conferring odds ratios (ORs) of 1.51 (95% confidence interval [CI], 1.31–1.74; P = 7.6 × 10⁻⁸), 1.58 (95% CI, 1.37–1.81; P = 4.7 × 10⁻¹⁰) and 1.20 (95% CI, 1.08–1.34; P = 0.002), respectively, compared with the highest quartiles (P trends < 0.001 observed for each). Similar associations were found for impaired lung function (FEV₁ <80% Global Lung Initiative predicted FEV₁: OR, 1.22, 95% CI, 1.11–1.33; P = 1.2 × 10⁻⁵; FEV₁/FVC <0.7: OR, 1.13, 95% CI, 1.03–1.24; P = 0.01). Lower lung function was associated with lower intraocular pressure (IOP), thinner macular retinal nerve fiber layer thickness, and thinner ganglion cell–inner plexiform layer thickness. No interactions were observed between glaucoma genetic risk and lung function. MR analyses did not suggest causal relationships.

Conclusions: Lower FVC, FEV₁, FEV₁/FVC, and impaired lung function are potential biomarkers for glaucoma risk. These findings may facilitate clinical strategies for glaucoma management, particularly for individuals with impaired lung function.

Glaucoma, a group of optic neuropathies characterized by typical visual field impairment and loss of retinal nerve fiber layer (RNFL) with cupping of the optic nerve head (ONH), is a leading cause of irreversible blindness projected to affect 111.8 million people by 2040.^1,2 Despite its high prevalence and devastating visual impact, glaucoma often goes undiagnosed until advanced stages.² Raised intraocular pressure (IOP) is an important risk factor for glaucoma development; however, approximately 25% to 50% of individuals with glaucoma have IOP within the normal range.^3,4 To date, elevated IOP is the only proven modifiable risk factor for glaucomatous optic nerve injury; biomechanical theory suggests that a high IOP may mechanically induce changes in the ONH.^2,5 Additionally, vascular theory suggests that insufficient blood supply to the ONH may impair optic nerve perfusion and oxygenation, resulting in a loss of retinal ganglion cells.⁵ However, no treatment currently can restore vision loss in glaucoma, making it important for the identification of other potentially modifiable risk factors of glaucoma. 

Impaired lung function has been illustrated as a predictor of morbidity and mortality and may predispose the development of multiple disease processes.⁶ Studies have reported that the retina and optic nerve may be affected by chronic obstructive pulmonary disease (COPD)-related hypoxemia.^7–9 COPD has been found to induce the development of hemodynamic disturbances and oxidative stress, which are implicated in the pathogenesis of glaucoma.¹⁰ In addition, obstructive sleep apnea (OSA), a chronic condition characterized by intermittent partial or complete upper airway obstruction during sleep leading to transient hypoxia and optic nerve hypoperfusion, is a known risk factor for glaucoma.^11,12 OSA is often featured in bidirectional interactions with obstructive lung diseases.¹³ However, to our knowledge, studies on the role of lung function in glaucoma have been scarce. 

Given that lung function is a modifiable factor and an important tool in general health assessment, we conducted a large observational study using the UK Biobank data to investigate the associations of lung function parameters with glaucoma and assess whether the genetic risk of glaucoma could influence these associations. We also used two-sample Mendelian randomization (MR) analyses to investigate the potential causal relationships underlying the detected associations. Moreover, we tested the potential relationships between lung function and glaucoma-related traits, including IOP and inner retinal thickness. 

Methods

UK Biobank Study Population

This study uses data from the UK Biobank cohort, the specifics of which have been detailed previously.¹⁴ The overall study protocol is available online (http://www.ukbiobank.ac.uk/resources/). Briefly, this population-based cohort study involved 502,248 participants aged 40 to 69 years recruited between 2006 and 2010 across 22 assessment centers throughout the UK, with genomic and phenotypic data collected.¹⁵ During their visits to the assessment centers, participants completed questionnaires, underwent physical and functional measurements, and provided biospecimens. The North West Multicenter Research Ethics Committee approved the study following the principles of the Declaration of Helsinki. The present study is conducted using the UK Biobank Resource under application number 91320. 

Ascertainment of Lung Function

The details of lung function measurement processing methods are provided in Supplementary Methods. The Global Lung Initiative (GLI) 2012 equations were used to determine reference lung volumes for forced expiratory volume in 1 second (FEV₁) and compute the percent predicted FEV₁.¹⁶ The volumetric measures of lung function assessed in this study included forced vital capacity (FVC), FEV₁, GLI-predicted FEV₁, and FEV₁/FVC ratio. Impaired lung function was defined as (1) an FEV₁ of less than 80% of GLI-predicted FEV₁ reference value; or (2) FEV₁/FVC of less than 0.7, following the guidelines for airflow obstruction grading according to the Global Initiative for Chronic Obstructive Lung Disease.¹⁷ 

Primary Outcome: Glaucoma

We adopted self-reported diagnoses and Hospital Episode Statistics data to define prevalent glaucoma, as previously described (Supplementary Methods).¹⁸ 

Secondary Outcomes: IOP and Inner Retinal Thickness

IOP data were collected from approximately 128,000 UK Biobank participants at six assessment centers during baseline recruitment. The IOP in each eye was measured once using the Ocular Response Analyzer noncontact tonometer (Reichert Corp, Depew, NY, USA). We adopted the corneal-compensated IOP, calculated from a linear combination of inward and outward applanation tensions,¹⁹ because it is less affected by corneal biomechanical properties.²⁰ The Supplementary Methods detail the data processing procedures for IOP. 

Baseline macular spectral-domain optical coherence tomography (OCT) imaging was performed on 67,321 UK Biobank participants between 2009 and 2010 using a Topcon 3D OCT1000 Mark II device.^21,22 The detailed OCT data ascertainment and quality control are provided in Supplementary Methods. The average thicknesses of the macular RNFL (mRNFL) and the ganglion cell–inner plexiform layer (mGCIPL) were calculated by averaging the values from both eyes when available. If only one eye met the quality criteria, measurements of this eye were used. 

UK Biobank Array Genotyping and Glaucoma Polygenic Risk Score Development

The genotyping methods used in the UK Biobank are provided in Supplementary Methods. We calculated the polygenic risk score (PRS) using 2673 independent genetic variants associated with glaucoma (P ≤ 0.001) from a prior multitrait analysis of a genome-wide association study (GWAS), as reported by Craig et al.,²³ which included the UK Biobank data. The multitrait PRS is thought to provide a more comprehensive representation of the genetic heterogeneity underlying glaucoma. The PRS was developed using the formula, \(\mathop \sum \nolimits_{i = 1}^{2673} {{\hat{\beta }}_{( i )}}*SN{{P}_{( i )}}\), with \({{\hat{\beta }}_i}\) representing the estimated effect size of SNP_(i) on glaucoma from the original GWAS. The PRS was standardized to have a mean of 0 and a standard deviation of 1 for analysis.^18,23 We used the receiver operating characteristic curve to assess the performance of the constructed PRS. 

Collection of Covariates Data

We retrieved the following data collected by the UK Biobank through a baseline survey for glaucoma risk factors reported in previous studies^24,25: age, sex, self-reported ethnicity, Townsend Deprivation Index (TDI), alcohol consumption, smoking status, physical activity (PA), particulate matter with aerodynamic diameters of less than 2.5 µm (PM2.5) (µg/m³), body mass index (BMI), systolic blood pressure (SBP), and a history of diabetes. Ethnicity based on self-reported data was grouped into GLI ethnic groups to use the GLI-2012 equations for predicted reference FEV₁ values.²⁶ The Supplementary Methods includes the definitions of these covariates. Supplementary Table S1 includes the definitions of diabetes. 

Statistical Analysis

Participant demographics based on glaucoma prevalence and lung function measures were summarized using descriptive statistics. Continuous variables were reported as mean ± SDs, and categorical variables as frequencies and percentages. We used multivariable logistic regression models to estimate the effect of lung function (treated as continuous variables) on glaucoma, adjusting for baseline age, sex, ethnicity, TDI, BMI, smoking status, alcohol consumption, PA, SBP, PM2.5, and history of diabetes. To investigate a potential nonlinear effect, we divided lung function measures (FVC, FEV₁, FEV₁/FVC, and GLI predicted FEV₁) into quartiles, using the fourth quartile (the highest group) as the reference and tested for trends using the median value of each group to further ensure robustness against potential outliers in the associations. In addition, we dichotomized lung function measures based on clinically established cutoffs (e.g., FEV₁ < 80% GLI predicted or FEV₁/FVC < 0.7). 

We also conducted interaction, subgroup, and sensitivity analyses for the primary outcome (glaucoma). First, we evaluated gene–lung function interactions within individuals of European ancestry, which were identified through principal component analysis. Interaction terms between the standardized PRS and continuous lung function measures were added to multivariable logistic regression models. These models adjusted for the same covariates, and the significance of the multiplicative interaction term was assessed using the Wald test. A subgroup analysis stratified by PRS tertiles (low [T1], intermediate [T2], and high [T3]) was also conducted. Second, we performed a sex-stratified analysis to investigate whether differences in factors, such as airway anatomy or hormonal influences, could lead to sex-specific variations in the relationship between impaired lung function and glaucoma. Third, sensitivity analyses were performed, including the exclusion of patients with asthma, OSA, or a history of COPD medications, as well as restricting the cases to hospital-diagnosed primary open-angle glaucoma (POAG) (Supplementary Methods). Fourth, to ensure the comparability of cases and controls, we matched the cohorts using propensity score matching, including all covariates from the main analysis. Propensity score matching was performed using 1:1 matching with the nearest neighbor greedy matching algorithm, applying a caliper of 0.1 standardized mean differences.²⁷ The associations between lung function measures and glaucoma were assessed using univariate logistic regression models in the matched dataset. 

We additionally assessed the association between lung function measures and secondary outcomes (glaucoma-related traits, including IOP, mGCIPL, and mRNFL). Using multivariable linear regression models, adjusting for the same covariates as in the main analyses. 

All hypothesis tests were two-sided and a P value of less than 0.05 was considered statistically significant for multivariable analyses. All statistical analyses were performed using the R software version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria). 

MR Analyses

Because POAG is the most common form of glaucoma, we conducted two-sample MR analyses to explore the potential causal association between lung function phenotypes and POAG. The main analysis used the inverse variance weighted method,²⁸ weighted median,²⁹ MR-Egger methods,³⁰ and MR pleiotropy residual sum and outlier³¹ methods for a comprehensive assessment. We extracted the summary-level statistics from the GWAS for the lung function (FEV₁, FVC, and FEV₁/FVC < 0.7)³² on 353,315 European UK Biobank population from Medical Research Council Integrative Epidemiology Unit OpenGWAS project³³ (https://gwas.mrcieu.ac.uk/) and POAG dataset (n = 399,805) from the FinnGen Consortium³⁴ (https://r10.finngen.fi/) for a two-sample MR with nonoverlapping samples. Full details of the MR methods are available in the Supplementary Methods. 

Results

Characteristics of Study Participants

The flowchart of participant inclusion is shown in Figure. A total of 3116 individuals were diagnosed with glaucoma at baseline. The demographic characteristics of the 85,369 participants grouped by quartiles of FEV₁/FVC are summarized in Table 1. The mean age at recruitment was 56.6 ± 8.1 years, and 55.7% were female. Compared with participants in the highest quartile of FEV₁/FVC, those with lower levels were more likely to be older, male, have a higher prevalence of glaucoma, and have more frequent lung function impairment. In addition, the PRS for POAG achieved an area under the curve of 0.77, indicating good predictive accuracy (data not shown). 

Figure.

 

Sample selection strategy. ATS, American Thoracic Society; ERS, European Respiratory Society; GLI, Global Lung Initiative.

Table 1.

 

Baseline Characteristics of Participants by Quartiles of FEV₁/FVC

 

Association of Lung Function With Glaucoma

We observed a significant inverse association between lung function measures and glaucoma, with odds ratios (ORs) of 0.86 (95% confidence interval [CI], 0.81–0.91) for FVC, 0.81 (95% CI, 0.75–0.87) for FEV₁, 0.49 (95% CI, 0.29–0.84) for FEV₁/FVC, and 0.994 (95% CI, 0.992–0.996) for GLI predicted FEV₁, all P values were less than 0.05 (Table 2). Comparing the highest quartile (Q4) of lung function measures, the lowest quartile had ORs of 1.51 (FVC, 95% CI, 1.31–1.74; P = 7.6 × 10⁻⁸), 1.58 (FEV₁, 95% CI, 1.37–1.81; P = 4.7 × 10⁻¹⁰), 1.20 (FEV₁/FVC, 95% CI, 1.08–1.34; P = 0.002), and 1.30 (GLI predicted FEV₁, 95% CI,1.17–1.44; P = 8.8 × 10⁻⁷) with all P for trends being less than 0.001 (Table 2). Impaired lung function was also associated with glaucoma (FEV₁ < 80% GLI predicted FEV₁, OR, 1.22, 95% CI, 1.11–1.33; P = 1.2 × 10⁻⁵; FEV₁/FVC < 0.7, OR, 1.13, 95% CI, 1.03–1.24; P = 0.01) (Table 2). Moreover, these associations remained consistent across various sensitivity analyses (Supplementary Tables S2–S5). Supplementary Table S6 shows the characteristics of each covariate before and after matching, which indicated no major differences after matching (standardized mean difference values < 0.1). In the matched dataset, the identified associations between lung function measures and glaucoma were still significant, except for FEV₁/FVC < 0.7 and FVC (Supplementary Table S7). 

Table 2.

 

Associations Between Lung Function Measures and Glaucoma

 

Genetic Predisposition × Lung Function and Subgroup Analysis Stratified by PRS

We tested for statistical interaction between glaucoma PRS and lung function measures (treated as continuous variables) on glaucoma but found no significant interaction (all P for interactions > 0.05; Supplementary Table S8). In subgroup analysis stratified by PRS, FEV₁ < 80% GLI predicted FEV₁ was associated with glaucoma in the intermediate and high genetic risk groups (all P < 0.05) and FEV₁/FVC < 0.7 was associated with glaucoma in the high genetic risk group (OR, 1.16; 95% CI; 1.02–1.32, P < 0.05) (Supplementary Table S8). 

Subgroup Analysis Stratified by Sex

Impaired lung function was significantly associated with glaucoma in males, as indicated by (FEV₁ < 80% of GLI predicted FEV₁, OR, 1.34, 95% CI, 1.19–1.51; P = 2.0 × 10⁻⁶; FEV₁/FVC < 0.7, OR, 1.14, 95% CI, 1.00–1.29; P = 0.046), but not in females (FEV₁ < 80% of GLI predicted FEV₁, OR, 1.10, 95% CI, 0.96–1.25; P = 0.2; FEV₁/FVC<0.7, OR, 1.11, 95% CI, 0.96–1.29; P = 0.2) (Supplementary Table S9). 

MR Analyses

For FVC, all MR approaches did not yield significant results (P > 0.05). Although the MR pleiotropy residual sum and outlier global test showed directional pleiotropy (P < 0.001) after excluding the outliers, indicating that overall horizontal pleiotropy might be present, the intercept test of MR-Egger indicated no pleiotropy (P = 0.5). Similarly, all primary MR analyses failed to establish a causal relationship between FEV₁, FEV₁/FVC of less than 0.7, and the risk of POAG. In addition, although some evidence of heterogeneity was observed (P < 0.05), all MR sensitivity analyses consistently indicated no significant causal association. Therefore, our findings did not reveal any significant causal relationship between lung function measures and POAG. Detailed MR analysis procedures and results are provided in the Supplementary Methods and Supplementary Tables S10 to S13. 

Association of Lung Function With Glaucoma-related Traits

To evaluate the association of lung function with glaucoma-related traits, we included 60,035 participants with available IOP data and 28,760 participants with inner retinal thickness measurements on OCT, that is, mRNFL and mGCIPL (Supplementary Table S14). Lower levels of lung function measures (FVC, FEV₁, GLI-predicted FEV₁, and FEV₁/FVC) were associated with reduced IOP (all P for trends < 0.05). Impaired lung function showed a similar association, where an FEV₁ of less than 80% of GLI predicted FEV₁ was associated with a lower IOP by −0.18 mm Hg (95% CI, −0.26 to −0.10; P = 3.1 × 10⁻⁶) and FEV₁/FVC of less than 0.7 with a lower IOP by −0.13 mm Hg (95% CI, −0.21 to −0.04; P = 0.004) (Table 3). 

Table 3.

 

Associations Between Lung Function Measures and Glaucoma-related Traits

 

Regarding OCT measurements, decreasing levels of FVC, FEV₁, FEV₁/FVC ratio, and GLI-predicted FEV₁ were associated with reduced mGCIPL thickness (β coefficients: 0.2 µm [95% CI, 0.10–0.30], 0.31 µm [95% CI, 0.18–0.43], 1.26 µm [95% CI, 0.21–2.30], and 0.01 µm [95% CI, 0.006–0.014], respectively; all P values < 0.05) (Table 3). Moreover, these associations remained significant when testing the difference in mGCIPL thickness across the quartiles of FVC, FEV₁ and GLI-predicted FEV₁ (all P for trends < 0.05), but the quartiles of FEV₁/FVC ratio became insignificant (P for trend > 0.05). Regarding impaired lung function, an FEV₁ of less than 80% of GLI-predicted FEV₁ was associated with a thinner mGCIPL by −0.33 µm (95% CI, −0.50 to −0.15; P = 2.1 × 10⁻⁴), whereas an FEV₁/FVC of less than 0.7 was associated with a thinner mGCIPL by −0.18 µm (95% CI, −0.37 to 0.016; P = 0.07) (Table 3). 

Similar associations were identified for mRNFL, where decreasing levels of all lung function measures, except FEV₁/FVC, were associated with reduced mRNFL thickness (all P for trends < 0.001). FEV₁ < 80% of GLI-predicted FEV₁ was associated with a thinner mRNFL by −0.35 µm (95% CI, −0.48 to −0.22; P = 9.1 × 10⁻⁸), whereas no significant association was found between an FEV₁/FVC of less than 0.7 and mRNFL (P > 0.05) (Table 3). 

Discussion

In this large-scale analysis of lung function with glaucoma, we identified an inverse association between lower levels of FVC, FEV₁, FEV₁/FVC, and GLI-predicted FEV₁, as well as impaired lung function (defined by FEV₁ < 80% GLI predicted FEV₁ or FEV₁/FVC <0.7), with glaucoma in the UK Biobank population. No interactions were observed between genetic predisposition to glaucoma and lung function measures, and MR analyses provided no evidence of a causal relationship between lung function and POAG. Regarding glaucoma-related traits, we found lower lung function to be associated with lower IOP, thinner mGCIPL, and thinner mRNFL. 

Previous research examining the effect of lung function on glaucoma has been scarce. Abnormality of visual evoked potentials were observed among patients with COPD, which could be due to peripheral neuropathy caused by COPD that also affects the optic nerve.^7,9,35 Ugurlu et al.³⁶ observed reduced RNFL thickness across all examined segments in patients with COPD, but the difference reached statistical significance only in the inferior segment. Peripheral arterial oxygen saturation changes were shown to be highly correlated with changes in retinal arterial oxygen saturation.³⁷ A constant supply of oxygen is crucial to meet the high energy demands of the retina and sustain its blood flow and normal retinal cellular function. These findings suggested a possible link between lung health and optic nerve health. COPD was shown to be an independent determinant of microvascular retinopathy.³⁸ Our present study demonstrated an inverse association between lung function and glaucoma, consistent with previous evidence. Moreover, OSA, a common respiratory disease, has been studied extensively for its potential link with glaucoma risk.^11,12 Such an association may support the pathophysiological mechanisms of optic nerve damage from transient hypoxia and hypoperfusion linked to OSA episodes.¹¹ Our sensitivity analysis, by excluding participants with OSA, remained consistent with the main results, suggesting that the inverse dose-response relationships between lung function and glaucoma were independent of OSA. Notably, results from the matched data decreased the residual influence of covariates, and the observed associations remained consistent with the main results, except for FVC and FEV₁/FVC of less than 0.7 (0.05 < P values < 0.1), which might be partially due to the reduced sample size. 

This study also found that the association between impaired lung function and glaucoma was predominantly observed in males. Although hormonal factors and differences in airway geometry between men and women may contribute to variations in the association between lung function and glaucoma, the precise nature of these differences remained largely unknown.³⁹ Hormone replacement therapy has been reported to be associated with better FEV₁ and FVC in elderly women.⁴⁰ In addition, accumulating evidence suggested that lung function could be more affected by inflammatory processes in males than in females.^41,42 This result may explain our findings that impaired lung function is associated with glaucoma more strongly in men. Another possibility is that the cumulative effects of smoking on lung function may differ by sex, given the greater prevalence of smoking observed in males in our study. Further research with more detailed measures of smoking history and intensity (e.g., pack-years), hormonal assessments, and other sex-specific exposures will be needed to clarify the underlying mechanisms driving these differences between males and females. 

Because OCT evaluation of the macula aids in assessing glaucoma, we tested the associations between lung function and mGCIPL and mRNFL. We found that lower levels of lung function measures were associated with thinner mGCIPL and mRNFL, except for the FEV₁/FVC ratio. Impaired lung function defined by an FEV₁ of less than 80% of the GLI-predicted FEV₁ was associated with thinner mGCIPL and mRNFL. These associations may be supported by the vascular theory, which holds that hypoxemia directly damages the optic nerve and retinal ganglion cells.^3,43 Impaired lung function can cause tissue hypoxia and chronic systemic inflammation, compromising optic nerve perfusion and oxygenation and leading to optic neuropathy.^8,44 Elevated hypoxia-induced endothelin-1 levels caused vascular dysregulation, impairing blood flow to the ONH and retina.⁴⁵ 

Considering the mechanical theory that elevated IOP may contribute to glaucoma development, we directly tested the association between lung function and IOP. Interestingly, we observed that better lung function was associated with a higher IOP, with a dose-dependent effect. A previous study found no significant difference in IOP between healthy controls and patients with COPD, but it was limited by relatively small sample sizes (35 cases and 35 controls) and the exclusion of a high number of patients with comorbidities.⁴⁶ IOP is determined by the balance between aqueous humor inflow and outflow. Participants with impaired lung function are more prone to systemic conditions such as hypoxemia, cardiovascular diseases, blood viscosity, and the metabolic syndrome,⁴⁷ which may influence systemic hemodynamics and nervous system activation, potentially affecting IOP through uncertain mechanisms. Alternatively, exogenous factors, such as lifestyle and diet, may also affect IOP.⁴⁸ Thus, residual confounding factors may influence the observed association between lung function and IOP. Nevertheless, the findings of the negative association between lung function and glaucoma, and the positive association between lung function and IOP, indicated that the effect of impaired lung function on glaucoma may not be mediated by IOP elevation, but is more likely to be influenced by systemic factors, such as ocular microvascular damage and ischemic changes, in line with the vascular theory.⁴⁹ Future studies should be warranted to explore the underlying mechanism of such association. 

The strengths of this study include the large sample size and population-based design from the UK Biobank cohort, which provided strong statistical power to examine these relationships while allowing the adjustment of multiple potential confounders. 

However, this study has several limitations. First, we relied on self-reported data because using only International Classification of Diseases codes to identify cases would capture only patients who required a procedure or inpatient stay, potentially missing those who were diagnosed as outpatients. Nevertheless, self-reported glaucoma status was adopted widely in previous studies.^18,25 Our study is limited further by its use of self-reported medications. The self-reported data collected via questionnaires may be subject to recall bias, misclassification bias, and social desirability bias. This factor may lead to outcome misclassification that influences our results. However, our sensitivity analysis restricted to patients with POAG identified through hospital-linked International Classification of Diseases diagnoses maintained consistent findings with the main findings. Second, the self-reported definition of glaucoma lacks specificity, which is a common limitation in large population-based studies where detailed phenotypic or disease information is often unavailable. For example, distinct subtypes like low-tension and high-tension glaucoma may have different pathological mechanisms, potentially leading to varying associations with lung function. Third, given the natural diurnal fluctuations of IOP, using single time-point IOP measurement may introduce variability that is insufficient to capture the true IOP pathology in glaucoma patients.⁵⁰ Future studies incorporating repeated IOP assessments and longitudinal designs could provide more comprehensive and reliable insights. Fourth, the lack of causal relationships between lung function and glaucoma (MR results) suggested that the observed associations may be due to residual confounding rather than biological associations, despite the efforts to control for confounding through multivariable adjustment and matching for potential confounding factors. Fully eliminating residual confounding remains a challenge in cross-sectional studies. Last, previous evidence suggested the presence of healthy volunteer selection bias in the UK Biobank, thus limiting its generalizability.⁵¹ However, a recent study has reported that the risk factor associations in the UK Biobank seem to be generalizable.⁵² Nevertheless, because glaucoma subtypes and genetic components vary across ethnic populations, further studies should be warranted to confirm the role of lung function impairment in the risk of glaucoma development. 

In summary, our findings indicate that participants with lower levels of lung function parameters and lung function impairment have a higher rate of glaucoma, although MR analyses showed no causal effect. Therefore, lung function may serve as a potential biomarker for glaucoma. Further studies are needed to confirm the role of lung function in glaucoma risk in other populations. 

Acknowledgments

The authors thank all the participants in this study and all the research grants that supported this work. 

Supported by the research grants from the Health and Medical Research Fund Hong Kong (07180256 [L.J.C.]); the General Research Fund, Hong Kong (14102122 & 14105320 [C.C.T.]); a Research Matching Grant (RMG) by the Hong Kong Government (8601668 [L.J.C.]); the Endowment Funds for Lam Kin Chung. Jet King-Shing Ho Glaucoma Treatment and Research Centre and Lim Por-Yen Eye Genetics Research Centre, Hong Kong SAR, China; the Chinese University of Hong Kong (CUHK) Jockey Club Myopia Prevention Programme (No grant no., [J.C.Y]); and the Chinese University of Hong Kong (CUHK) Jockey Club Children Eye Care Programme (No grant no., [J.C.Y]). 

Disclosure: J. Yu, None; Y. Zhang, None; K.W. Kam, None; M. Ho, None; A.L. Young, None; C.P. Pang, None; C.C. Tham, None; J.C. Yam, None; L.J. Chen, None