Abstract
Introduction
In order to better understand comorbidity rates and the associated risk of death in patients with glaucoma we retrospective analyzed two groups of subjects aged 50 years and above residing in Lombardy Region (Northern Italy) following them from January 1, 2017 to February 1, 2020 (just before the COVID-19 pandemic started in Italy). The two groups were all subjects with incident glaucoma in 2017 and a 3:1 random sample stratified by age and sex of subjects without glaucoma. Main outcome was overall survival. Other outcomes were incidence of cardiovascular diseases (heart attack, stroke, and peripheral arterial disease).
Methods
All data were taken from Lombardy Region administrative database and therefore the presence of glaucoma was ascertained using antiglaucoma drug prescriptions, being hospitalized or undergoing an intervention for glaucoma, and having a glaucoma exemption for healthcare co-payments.
Results
The study identified 14,138 incident cases of glaucoma and selected 42,414 subjects without glaucoma. The number of deaths was higher among glaucoma subjects (11.6%) compared to those without glaucoma (10.5%). The death hazard ratio (HR) for glaucoma subjects compared to controls was 1.07 (95% CI 1.01–1.14, p = 0.015) in the adjusted model. Similarly incidence of stroke was higher in glaucoma compared to non-glaucoma subjects (HR 1.10, 95% CI 1.01–1.20), while the incidence of heart attack and peripheral arterial disease during the follow-up period was similar in the two groups. HRs varied by age classes for death and peripheral arterial disease. Individuals with glaucoma showed higher comorbidity rates compared to those without glaucoma, particularly in diabetes mellitus (16.7% vs. 11.0%) and hypertension (62.2% vs. 58.1%).
Conclusions
Our results show an increased risk of mortality in subjects with glaucoma compared to those without glaucoma, with age being a significant factor influencing outcomes. These findings suggest the importance of monitoring and managing comorbidities in individuals with newly incident glaucoma to potentially improve their overall health outcomes and quality of life.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12325-025-03282-9.
Keywords: Glaucoma, General mortality, Stroke, Heart attack, Peripheral artery disease
Key Summary Points
| Why carry out this study? |
| Glaucoma is a leading cause of irreversible blindness worldwide, with its prevalence projected to rise significantly by 2040. |
| The association between glaucoma, cardiovascular diseases (CVD), and mortality has not been extensively studied in Europe, despite evidence suggesting shared vascular risk factors. |
| The study aimed to evaluate the relationship between glaucoma, CVD (heart attack, stroke, and peripheral arterial disease), and mortality in a large population-based cohort in Lombardy, Italy. |
| What was learned from the study? |
| Individuals with glaucoma had a higher risk of mortality (HR 1.07, 95% CI 1.01–1.14) and stroke compared to those without glaucoma, particularly in younger age groups (50–64 years). |
| Patients with glaucoma exhibited higher comorbidity rates, including diabetes (16.7% vs. 11.0%) and hypertension (62.2% vs. 58.1%), compared to controls. |
| The findings highlight the importance of monitoring and managing comorbidities in patients with glaucoma, especially in younger individuals, to potentially reduce mortality and stroke risk. |
| While no significant association was found between glaucoma and heart attacks or peripheral arterial disease, the study underscores the need for further research into the vascular mechanisms linking glaucoma to systemic health outcomes. |
Introduction
Glaucoma consists of a group of progressive optic neuropathies leading to the loss of retinal ganglion cells and their axons, which results in structural changes in the optic nerve head and visual field defects [1]. The global number of glaucoma cases has seen a rapid rise in recent decades and is projected to reach 112 million by 2040 [2]. Managing glaucoma is essential for preventing further vision loss due to its uncertain prognosis, requiring lifelong care and monitoring [3].
Studies show a link between glaucoma and systemic vascular conditions [4]. The pathophysiology of glaucoma involves vascular elements affecting ocular perfusion and intraocular pressure regulation [5], with disrupted autoregulation and endothelial dysfunction [5, 6]. There is evidence of circulatory disturbances in both systemic and local systems in individuals with glaucoma, especially at the level of the optic nerve head [7]. Patients with glaucoma have increased risks of cardiovascular disease (CVD)-related mortality and higher rates of CVD and related comorbidities compared to subjects without glaucoma [8].
While vascular dysregulation in glaucoma is well documented, the associations with specific cardiovascular outcomes remain heterogeneous. For stroke, prior studies suggest shared microvascular pathology: retinal ganglion cell loss in glaucoma and cerebral small-vessel disease in stroke both involve endothelial dysfunction and impaired autoregulation [6, 7, 9]. However, cohort studies report conflicting results—some show elevated stroke risk in open-angle glaucoma (OAG) (HR 1.3–1.5) [10], while others find no association [11], likely due to variability in glaucoma subtype inclusion (e.g., normal tension glaucoma (NTG) vs. primary open-glaucoma (POAG)) or insufficient adjustment for nocturnal hypotension. For myocardial infarction (MI), the evidence is even less consistent. The UK Biobank reported a 20% higher MI risk in patients with glaucoma [8], but this included mixed subtypes and lacked incident OAG stratification [8]. In contrast, the Beaver Dam Eye study found no MI–OAG link after adjusting for smoking and hypertension [12]. Notably, peripheral arterial disease (PAD) is understudied in glaucoma, though both conditions involve systemic vascular insufficiency [13].
Furthermore, associations between glaucoma and survival have not been extensively studied in Europe, in part because of the cost of population-based investigations of this eye disease, which occurs less frequently than other ophthalmologic conditions such as cataracts and macular degeneration [12, 14–17]. The Wisconsin Epidemiologic Study of Diabetic Retinopathy found reduced survival in diabetic patients with diabetes and a history of glaucoma or clinically confirmed neovascular glaucoma compared to subjects without glaucoma [15]. Similar results were reported in the Framingham Eye Study cohort: intraocular pressures > 25 mmHg, a history of glaucoma treatment, or both were associated with reduced survival [18]. Furthermore, the long-term survival rates of hospitalized patients with glaucoma in Oslo were lower than those of the general population of Norway [19].
However, a meta-analysis of nine studies presented no convincing evidence for a higher risk of all-cause mortality among those affected with POAG [11].
Given the predicted increase in the incidence of glaucoma worldwide and the uncertainty of the evidence concerning higher comorbidity rates and the associated risk of death, there is a need for further research for patients with glaucoma.
The main aim of the current study was to evaluate the relationship between glaucoma and mortality. As a secondary aim we wanted to study the relationship between glaucoma and CVD.
Methods
Data Sources
Subject Selection and Database
Data were extracted from the administrative database of the Lombardy Region. Lombardy, situated in Northern Italy, is home to around 10 million residents. Healthcare in Italy is predominantly publicly funded, and these data are collected for administrative and reimbursement purposes. The database contains demographic details such as sex and age, information on medication dispensing, exemptions from healthcare co-payment, records of hospital admissions with primary and secondary diagnoses, and details of medical interventions within hospitalization.
Data Classification
Medication data were categorized using the Anatomical Therapeutic Chemical (ATC) Classification system. Hospital diagnoses and interventions were coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) classification. Exemptions from co-payment were identified using specific unique codes.
Data Accessibility and Agreement
The data utilized in the study were made available for analysis owing to a collaborative agreement between the Lombardy Region and the Mario Negri Institute aimed at evaluating the appropriateness of drug utilization and analyzing treatment pathways. All data were managed in accordance with current Italian privacy laws.
Study Cohort
The algorithm employed to identify incident glaucoma subjects in 2015 was based on the presence of one or more of the following criteria:
Receipt of at least six packages of antiglaucoma drugs (ATC code S01E) within a year.
Hospitalization at least once with an ICD-9-CM diagnosis code of 365.1.
Undergoing a glaucoma intervention with ICD-9-CM codes 12.79 or 12.65.
Having an exemption related to glaucoma for healthcare co-payments with code 019.365.1.
Additionally, a random control sample, stratified by age and sex at a ratio of 3:1, consisting of subjects without glaucoma was also selected.
In order to study incident patients, subjects with glaucoma between 2010 and 2014 were excluded from the analysis.
Subjects were monitored from January 1, 2017 to February 1, 2020, predating the onset of the COVID-19 pandemic, which significantly impacted the Lombardy Region. The outcomes considered were death, heart attack (hospital diagnosis of ICD-9-CM 410*), stroke (hospital diagnosis of ICD-9-CM 430-348), and PAD (hospital diagnosis of ICD-9-CM 093.0, 437.3, 440*, 441*, 443*, 447.1, 557.1, 557.9, V43.4).
The primary covariates assessed included diabetes mellitus (DM) (based on at least two A10 prescriptions in 2014), cholesterol elevation (indicated by statin prescriptions, with a minimum of two C10AA prescriptions in 2014), antithrombotic use necessity (at least two B10A prescriptions in 2014), and hypertension [20].
A sensitivity analysis was conducted by detecting patients with glaucoma using a higher threshold of a minimum of 10 packages of S01E drugs received within a year (eMethods).
Informed Consent
The study was carried out in accordance with the Declaration of Helsinki. The study was exempt from patients informed consent, and it was waived according to the General Authorization for the Processing of Personal Data for Scientific Research Purposes Issued by the Italian Privacy Authority on August 10, 2018. (https://www.garanteprivacy.it/home/docweb/-/docweb-display/docweb/9124510#5).
Furthermore, the study provided sufficient guarantees of individual records anonymity, with respect to the Italian privacy law.
Ethical Approval
According to the Italian law, studies using anonymous data from administrative databases that do not involve direct access to individual patient data need no approval from or notification by an ethics committee/institutional review board, and informed patient consent is not required. All data were managed according to Italian law on privacy.
Statistical Analysis
The patient cohort included in the analyses was described using means and standard deviations for continuous characteristics and numbers with percentages for categorical ones. Kaplan–Meier curves were generated, and a log-rank test was conducted to assess differences between subjects with and without glaucoma. Subsequently, Cox proportional hazard models were applied to determine hazard ratios (HRs) along with their 95% confidence intervals (95% CI). To examine the differential effect of glaucoma by sex and age class, separate sub-analyses were conducted for sex (with females as the reference group) and age groups (50–64, 65–79, and 80 and older, where the last of these served as the reference group), while the significance of the interaction effect was explored by separately incorporating interaction terms for glaucoma with sex and age groups in the models, using females and individuals aged 80 and above as the respective reference categories. The assumption of proportional hazards was verified by assessing the Schoenfeld residuals test, revealing no violations of this assumption. Significance was set at 0.05, two sided. Statistical analyses were carried out using SAS software (SAS Institute Inc., Cary, NC, USA).
Results
The population of Lombardy on December 31, 2016 included approximately four million subjects aged 50 years or more. We identified 14,138 incident cases of glaucoma and selected 42,414 subjects without glaucoma in a 3:1 random sample stratified by age and sex (Fig. 1).
Fig. 1.
Flowchart of the study
Subjects with glaucoma were predominantly female (56.1%) and had an average age of 72.7. Individuals with glaucoma showed higher comorbidity rates compared to those without glaucoma, particularly in DM (16.7% vs. 11.0%) and hypertension (62.2% vs. 58.1%). However, both groups had similar histories of heart attacks, strokes, and PAD hospitalizations in preceding years (Table 1).
Table 1.
Characteristics of the cohorts at baseline
| With glaucoma | Without glaucoma | |
|---|---|---|
| N = 14,138 | N = 42,414 | |
| Male, no. (%) | 7927 (43.9) | 18,633 (43.9) |
| Female, no. (%) | 6211 (56.1) | 23,781 (56.1) |
| Age, mean (SD) | 72.7 (10.1) | 72.7 (10.1) |
| Age band, no. (%) | ||
| 50–54 | 741 (5.2) | 2223 (5.2) |
| 55–59 | 973 (6.9) | 2919 (6.9) |
| 60–64 | 1341 (9.5) | 4023 (9.5) |
| 65–69 | 2113 (15.0) | 6339 (15.0) |
| 70–74 | 2357 (16.7) | 7071 (16.7) |
| 75–79 | 2791 (19.7) | 8373 (19.7) |
| 80–84 | 2157 (15.3) | 6471 (15.3) |
| 85–89 | 1186 (8.4) | 3558 (8.4) |
| 90–94 | 407 (2.9) | 1221 (2.9) |
| 95–99 | 67 (0.5) | 201 (0.5) |
| 100+ | 5 (< 0.1) | 15 (< 0.1) |
| Diabetes, no. (%) | 2367 (16.7) | 4462 (11.0) |
| Antithrombotic agent, no. (%) | 3615 (25.6) | 9937 (23.4) |
| Statin, no. (%) | 3509 (24.8) | 9240 (21.8) |
| Hypertension, no. (%) | 8788 (62.2) | 24,637 (58.1) |
| Previous disease (from 2010 to 2014) | ||
| Heart attack, no. (%) | 202 (1.4) | 648 (1.5) |
| Stroke, no. (%) | 602 (4.3) | 1711 (4.0) |
| Peripheral arterial disease, no. (%) | 255 (1.8) | 661 (1.6) |
The proportion of deaths was higher among glaucoma subjects (11.6%) compared to those without glaucoma (10.5%) (Table 2). Survival curves statistically significantly differed (log-rank test: p = 0.0005) (Supplementary Fig. 1, first panel).
Table 2.
Risk of selected outcomes by glaucoma and by sex or age class
| Incident outcome | Model | All | Females | Males | 50–64 | 65–79 | 80+ |
|---|---|---|---|---|---|---|---|
| Glaucoma/no glaucoma subjects | 14,138/42,414 | 7927/23,781 | 6211/18,633 | 3055/9165 | 7261/21,783 | 3822/11,466 | |
| Survival | Deceased glaucoma/no glaucoma subjects (%) | 1637/4466 (11.6%/10.5%) | 832/2270 | 805/2196 | 95/204 | 591/1429 | 951/2833 |
| Unadjusted HR | 1.11 (1.04–1.17) | 1.10 (1.02–1.19) | 1.11 (1.02–1.20) | 1.41 (1.10–1.79) | 1.25 (1.14–1.38) | 1.00 (0.93–1.08) | |
| Fully adjusted HRa | 1.07 (1.01–1.14) | 1.06 (0.98–1.15) | 1.08 (1.00–1.17) | 1.27 (0.99–1.63) | 1.19 (1.08–1.31) | 0.99 (0.92–1.07) | |
| Heart attack | Incident glaucoma/no glaucoma subjects (%) | 211/607 (1.5%/1.4%) | 104/262 | 107/345 | 24/67 | 108/303 | 79/237 |
| Unadjusted HR | 1.05 (0.90–1.23) | 1.20 (0.95–1.50) | 0.94 (0.75–1.16) | 1.08 (0.68–1.72) | 1.08 (0.87–1.35) | 1.00 (0.77–1.29) | |
| Fully adjusted HRa | 0.98 (0.84–1.15) | 1.09 (0.87–1.37) | 0.89 (0.72–1.11) | 0.96 (0.60–1.55) | 1.00 (0.80–1.25) | 0.95 (0.74–1.23) | |
| Stroke | Incident glaucoma/no glaucoma subjects (%) | 717/1897 (5.1%/4.5%) | 371/984 | 346/913 | 49/86 | 323/857 | 345/954 |
| Unadjusted HR | 1.14 (1.05–1.25) | 1.14 (1.01–1.28) | 1.15 (1.01–1.30) | 1.73 (1.21–2.45) | 1.14 (1.01–1.30) | 1.09 (0.96–1.23) | |
| Fully adjusted HRa | 1.10 (1.01–1.20) | 1.09 (0.97–1.23) | 1.11 (0.98–1.25) | 1.42 (0.99–2.03) | 1.09 (0.96–1.24) | 1.07 (0.95–1.21) | |
| Peripheral arterial disease | Incident glaucoma/no glaucoma subjects (%) | 203/584 (1.4%/1.4%) | 75/213 | 128/371 | 30/46 | 120/306 | 53/232 |
| Unadjusted HR | 1.05 (0.89–1.23) | 1.06 (0.81–1.38) | 1.04 (0.85–1.28) | 1.97 (1.24–3.12) | 1.19 (0.96–1.47) | 0.68 (0.51–0.92) | |
| Fully adjusted HRa | 0.96 (0.82: 1.12) | 0.97 (0.74–1.26) | 0.95 (0.78–1.16) | 1.39 (0.87–2.23) | 1.09 (0.88–1.35) | 0.65 (0.48–0.88) |
HR hazard ratio
aAge, sex, diabetes, antithrombotic agent, statin, and hypertension adjusted
The death HR for glaucoma subjects compared to controls was 1.11 (95% CI 1.04–1.17, p = 0.0005) in univariate models and 1.07 (95% CI 1.01–1.14, p = 0.015) in fully adjusted models (Table 3).
Table 3.
Risk of incident outcomes in glaucoma vs. no glaucoma subjects
| Incident outcome | Unadjusted HR (95% CI) |
Age and sex adjusted HR (95% CI) |
Fully adjusteda HR (95% CI) |
|---|---|---|---|
| Survival | 1.11 (1.04–1.17) | 1.10 (1.04–1.17) | 1.07 (1.01–1.14) |
| Heart attack | 1.05 (0.90–1.23) | 1.05 (0.90–1.23) | 0.98 (0.84–1.15) |
| Stroke | 1.14 (1.05–1.25) | 1.14 (1.05–1.24) | 1.10 (1.01–1.20) |
| Peripheral arterial disease | 1.05 (0.89–1.23) | 1.05 (0.89–1.23) | 0.96 (0.82–1.12) |
HR hazard ratio
aAge, sex, diabetes, antithrombotic agent, statin, and hypertension adjusted
Subgroup analysis by age indicated that individuals aged 50–64 with glaucoma had the highest risk of death vs. subjects without glaucoma (Table 2 and Supplementary Fig. 2). The fully adjusted HRs were 1.27, 1.19, and 0.99 for the 50–64, 65–79, and 80+ age groups, respectively. There was no observable difference based on sex (Table 2 and Supplementary Fig. 3).
When death HR (glaucoma vs. no glaucoma) was compared among different age classes, HR of subjects aged 50–64 and 65–97 was significantly higher compared to the oldest class; no differential effect was observed by sex (Table 4).
Table 4.
Risk of selected outcomes with interaction by sex or age class
| Incident outcome | Case/control interaction with | Unadjusted | Fully adjusteda | ||
|---|---|---|---|---|---|
| HR (95% CI) | p value | HR (95% CI) | p value | ||
| Survival | Sex | 0.939 | 0.863 | ||
| F | 1 | 1 | |||
| M | 1.00 (0.90–1.12) | 1.01 (0.90–1.13) | |||
| Age | < 0.001 | 0.001 | |||
| 50–64 | 1.40 (1.09–1.81) | 1.34 (1.04–1.73) | |||
| 65–79 | 1.25 (1.11–1.41) | 1.23 (1.09–1.38) | |||
| 80+ | 1 | 1 | |||
| Heart attack | Sex | 0.126 | 0.114 | ||
| F | 1 | 1 | |||
| M | 0.78 (0.57–1.07) | 0.78 (0.57–1.06) | |||
| Age | 0.889 | 0.946 | |||
| 50–64 | 1.08 (0.64–1.84) | 1.01 (0.60–1.73) | |||
| 65–79 | 1.08 (0.77–1.52) | 1.06 (0.76–1.48) | |||
| 80+ | 1 | 1 | |||
| Stroke | Sex | 0.929 | 0.919 | ||
| F | 1 | 1 | |||
| M | 1.01 (0.85–1.20) | 1.01 (0.85–1.20) | |||
| Age | 0.051 | 0.098 | |||
| 50–64 | 1.59 (1.09–2.30) | 1.50 (1.04–2.18) | |||
| 65–79 | 1.05 (0.88–1.26) | 1.04 (0.87–1.24) | |||
| 80+ | 1 | 1 | |||
| Peripheral arterial disease | Sex | 0.921 | 0.888 | ||
| F | 1 | 1 | |||
| M | 0.98 (0.71–1.37) | 0.98 (0.70–1.36) | |||
| Age | < 0.001 | 0.001 | |||
| 50–64 | 2.89 (1.67–5.00) | 2.58 (1.49–4.46) | |||
| 65–79 | 1.74 (1.21–2.51) | 1.68 (1.17–2.42) | |||
| 80+ | 1 | 1 | |||
HR hazard ratio (patients with glaucoma/non-glaucoma subjects)
aAge/sex, diabetes, antithrombotic agent, statin, and hypertension adjusted
The incidence of stroke was higher in glaucoma subjects compared to controls. The unadjusted HR was 1.14 and the fully adjusted HR was 1.10 (95% CI 1.01–1.20) for subjects with glaucoma (Table 3 and Supplementary Fig. 1). Although youngest subjects showed a higher HR compared to older individuals (Table 2), overall statistical significance was not reached (Table 4).
Over the 3-year follow-up period, 1.5% of subjects with glaucoma and 1.4% of subjects without glaucoma experienced a heart attack. These rates did not differ significantly between the two groups based on unadjusted and adjusted models (Table 3). There was no apparent variation by age or sex in subgroup analyses or models with interactions (Tables 2 and 4, Supplementary Figs. 2 and 3).
The incidence of PAD during the follow-up period was similar in subjects with and without glaucoma (1.4%), with HRs indicating neutrality (fully adjusted HR 0.96) (Table 3). There was no significant impact of sex on the relationship between glaucoma and PAD (Tables 2 and 4, Supplementary Fig. 3). However, subgroup analyses revealed that the oldest subjects with glaucoma had a lower risk of PAD compared to their counterparts without glaucoma while youngest subjects had a higher risk (Table 2 and Supplementary Fig. 2). Similarly, in models with interactions, the youngest subjects with glaucoma were over twice as likely to be hospitalized with PAD compared to the oldest subjects (HR 2.58) (Table 4).
Sensitivity Analyses
The sensitivity analysis provided further confirmation of the previously reported results. The study population decreased by approximately 40% as a result of the stricter criteria required to select patients with incident glaucoma.
Demographic and clinical characteristics were similar to those previously reported (Supplementary Fig. 4 and Supplementary Table 1). The proportions of subjects experiencing different outcomes remained consistent in this subgroup.
The risk of death remained elevated in this subgroup (Supplementary Table 2), with the death HR in the youngest patients increasing by 76% relative to the oldest individuals (Supplementary Tables 3 and 4 and Supplementary Fig. 5).
The estimate of stroke HR remained high but did not reach statistical significance (p value in fully adjusted model 0.0570), with narrower differences observed between age groups.
Overall, PAD did not show a significant association with glaucoma. However, the oldest patients with glaucoma exhibited a lower risk (and the youngest a higher risk) relative to non-glaucoma subjects, with a notable age–glaucoma interaction (Supplementary Table 4).
Discussion
Our results in a large cohort show that patients with incident glaucoma have higher stroke and mortality risk compared to subjects without glaucoma and highlighted that glaucoma was particularly associated with an increase in risk in lower age classes.
Increased mortality risk among adult patients diagnosed with glaucoma may be mainly attributable to shared risk factors that contribute to both glaucoma and specific cause-related mortality. The presence of risk factors known to elevate the likelihood of both glaucoma and major cause-specific mortality, such as cardiovascular and neurological diseases, may further account for the increased mortality rates seen in this population [8, 10, 13].
Our results demonstrate a differential impact based on age, revealing that patients with glaucoma aged 50–64 years face a statistically significant 34% increased risk of mortality compared to their older counterparts. Furthermore, individuals aged 65–79 years exhibit a 23% elevated risk, albeit lower than that observed in the younger group. The findings indicate that sex does not significantly affect mortality risk among patients with glaucoma, suggesting that both male and female individuals may require comparable levels of clinical monitoring and intervention. A possible explanation might be that younger patients may have different patterns of healthcare utilization compared to older individuals. They may be less likely to engage in routine medical care or adhere to treatment protocols, potentially resulting in poorer disease management and higher associated mortality risk. Further investigations are essential to validate and elucidate the implications of these findings.
Our results for stroke in glaucoma aligns with meta-analyses, supporting ischemic mechanisms (e.g., nocturnal hypotension or microemboli) common to OAG and cerebrovascular disease [9]. The stronger association in younger patients (HR 1.42 for ages 50–64) may reflect earlier vascular dysregulation, as seen in NTG cohorts [21].
Among individuals with glaucoma compared to those without it, the current study also observed a statistically significant increase in the incidence of stroke. Although individuals with glaucoma exhibited a heightened risk of stroke, this risk varied across different age groups, with younger subjects displaying a higher HR for stroke. In a recent meta-analysis of seven studies involving 362,267 participants, it has been shown that glaucoma was associated with an increased risk of stroke [9]. The results suggest that patients with glaucoma need to be assessed for the risk of stroke to reduce the incidence of stroke. The association between OAG and stroke risk may also be attributed to shared risk factors, particularly in younger patients. While the exact mechanism underlying glaucomatous optic neuropathy remains unclear, ischemic damage is known to play a significant role in optic nerve damage associated with glaucoma [22]. Vascular impairment can lead to vascular dysregulation and ineffective autoregulation of ocular blood flow, particularly in glaucoma and NTG [6]. This dysregulation ultimately results in ischemic optic nerve damage and glaucomatous optic neuropathy [7]. Common risk factors for stroke, such as hypertension [23] and diabetes mellitus [24], are also related to OAG [25]. Further research and studies would be needed to definitively establish the reasons behind the higher HR for stroke in younger subjects with glaucoma.
Our data did not demonstrate any significant differences in the incidence of MI and PAD between individuals with glaucoma and those without it throughout the follow-up period. The absence of MI risk contrasts with UK Biobank findings [8], possibly because we excluded pre-existing CAD and analyzed MI separately. MI may rely more on macrovascular atherosclerosis (less tied to OAG) than stroke’s microvascular component. Residual confounding by smoking (unmeasured here) could also attenuate associations. Moreover, our analysis revealed that older subjects with glaucoma had a reduced risk of developing PAD when compared to their age-matched counterparts without glaucoma. The paradoxical PAD risk reduction in older OAG patients (HR 0.65) but elevation in younger (HR 1.39) may reflect survival bias (severe PAD patients dying before glaucoma onset) or age-dependent vascular adaptations. This warrants study of OAG–PAD overlap in vascular autoregulation genes [7, 26].
Moreover, these discrepancies may be due, in part, to the distinction that our study exclusively evaluated heart attacks, while the UK Biobank cohort [8] examined coronary artery disease (CAD), myocardial infarction, and ischemic stroke collectively under the definition of CVD.
Furthermore, the prolonged follow-up duration in the UK Biobank cohort [8], with a mean follow-up of 8.9 years, could elucidate the observed differences in outcomes. Nonetheless, our findings imply that while glaucoma may be correlated with an elevated risk of CVD in general, it does not appear to specifically predispose individuals to an increased likelihood of experiencing heart attacks compared to those without glaucoma.
Other interesting results come from the comparison of specific conditions of patients with glaucoma relative to subjects without glaucoma, showing that the former have higher comorbidity rates, particularly diabetes and hypertension. While existing studies indicate a connection between diabetes mellitus and POAG, the exact nature of this relationship is not yet fully understood. For instance, the Blue Mountains Eye Study found significant associations between diabetes mellitus and POAG after adjusting for factors like age and gender [27]. Similarly, the Beaver Dam Eye Study reported a comparable connection [28]. However, contrary findings were observed in the Tayside Study [29] and the Baltimore Eye Study, which did not support this association [30]. Our results further support the hypothesis that diabetes is more prevalent in patients with glaucoma when compared with individuals not affected by glaucoma. The heterogeneity among these studies suggests that the relationship between diabetes mellitus and POAG is complex and may vary across different study populations. Further research is needed to elucidate the true nature of this relationship, and determine the extent of the impact of diabetes on POAG development and progression. In our study, there was an association of systemic hypertension in individuals with glaucoma compared with those without glaucoma. The relationship between glaucoma onset or progression and arterial blood pressure (BP) is intricate, with researches showing that both low and high BP can serve as either protective factors or risks [21, 23, 31–35]. The influence of BP on glaucoma risk has been highlighted through 24-h ambulatory BP monitoring. Nocturnal hypotension is seen as a potential systemic vascular risk factor for glaucoma [36]. Moreover, irregular circadian rhythms, such as high nighttime BP, absence of nocturnal BP dips, or extreme nighttime BP drops, are associated with damage to target organs and heightened cardiovascular risk [23]. Despite the growing body of evidence regarding BP dysregulation, making clinical decisions based on this evidence is still challenging because of contradictory results, and a limited understanding of the complex dynamics between glaucoma and BP.
Our findings highlight also the importance of developing targeted preventive and management strategies for patients with glaucoma, particularly those within high-risk age groups. Additional research is necessary to investigate the underlying mechanisms of this association and to formulate effective interventions aimed at reducing mortality risk within this population.
A limitation of our study is linked to its population: since this was performed in a cohort of patients in Northern Italy its results may not be generalizable to other populations, especially those of Asian and African descent.
Recent global studies highlight similar glaucoma–CVD associations by subtype and population. For example, in a retrospective nationwide Korean study, patients who were diagnosed with OAG were more likely to experience subsequent stroke than a comparison group without OAG [37]. Furthermore, another recent nationwide Korean study observed that among individuals with suspected glaucoma, having cardiovascular risk factors/disease was associated with higher risk of developing POAG [38].
Moreover, in the African-Caribbean population of the Barbados Eye Studies [39] cardiovascular mortality tended to increase in persons with previously diagnosed OAG and ocular hypertension. However the link between cardiovascular complications and glaucoma in subjects of African descent remains uncertain, highlighting the need for further research [40].
As is customary for studies conducted on administrative databases this study has specific advantages and disadvantages. Since this database was established in order to pay for specific health services, its secondary use to answer clinical questions suffers from the lack of customary variables such as lifestyle conditions, and also from milder forms of diseases that were not yet ascertained. However, since national health system coverage in Italy is available to everyone, and the database encompasses an entire region, recruitment biases should not be present, and the large size of the studied population allows us to obtain results with narrow confidence intervals.
Moreover, our study relied on administrative databases, which lack detailed clinical records to differentiate glaucoma subtypes. This is a common constraint in large-scale epidemiological studies using claims data, as previously noted in similar work [8]. However, several factors support the predominance of OAG in our cohort: diagnostic codes (ICD-9-CM 365.1), treatment patterns (first-line therapies (e.g., S01E ATC drugs) are typically prescribed for OAG), and demographics: angle-closure glaucoma is less prevalent in European populations like Lombardy.
Conclusions
Individuals with glaucoma in the Lombardy Region demonstrated an increased risk of mortality compared to those without glaucoma, with age being a significant factor influencing outcomes. The findings suggest the importance of monitoring and managing comorbidities in individuals with incident glaucoma to potentially improve their overall health outcomes and quality of life. Further research is warranted to better understand the underlying mechanisms linking glaucoma with these adverse health outcomes.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank Giuseppe Preziosi, Monica Arivetti and Giovanna Rigotti from ARIA S.p.A, Alfredo Bevilacqua from Laife Reply S.p.A., Marco D. Forlani from T Bridge—BV Tech S.p.A and Igor Monti from Istituto di Ricerche Farmacologiche Mario Negri IRCCS who kindly assisted us with data collection.
Author Contributions
Conceptualization: Luciano Quaranta, Alessandro Nobili, Mauro Tettamanti, Robert N Weinreb. Formal analysis: Mauro Tettamanti, Alessia Antonella Galbussera, Luca Pasina, Mikołaj Kuźniak. Methodology: All authors. Data Curation: Mauro Tettamanti, Alessia Antonella Galbussera, Alessio Novella. Supervision: Ida Fortino, Olivia Leoni. Writing-original draft: Luciano Quaranta, Alessandro Nobili, Mauro Tettamanti. Writing-review and editing: All authors reviewed the drafted work and approved the final manuscript.
Funding
The study was carried out thanks to the contribution of Regione Lombardia (grant/award number DGR n. XII/1730—11 January 2024) and the Health Ministry of Lombardy (grant/award number EPIFARM-Pharmacoepidemiology). No funding or sponsorship was received for the publication of this article.
Data Availability
The datasets analyzed during the current study are not publicly available since they are administrative data property of the Lombardy Region.
Declarations
Conflict of Interest
Luciano Quaranta received payment honoraria for educational events by Thea, Abbvie and Omikron. Robert N Weinreb received grants from NIH and Research to Prevent Blindness. Luciano Quaranta is an Editorial Board member of Advances in Therapy. Luciano Quaranta was not involved in the selection of peer reviewers for the manuscript or any of the subsequent editorial decisions. Alessia A. Galbussera, Mauro Tettamanti, Alessio Novella, Luca Pasina, Ida Fortino, Olivia Leoni, Francesco Oddone, Sara Giammaria, Mikołaj Kużniak, and Alessandro Nobili have nothing to disclose.
Ethical Approval
According to the Italian law, studies using anonymous data from administrative databases that do not involve direct access to individual patient data need no approval from or notification by an ethics committee/institutional review board, and informed patient consent is not required. All data were managed according to Italian law on privacy.
Footnotes
The original publication was revised due to update in affiliations.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Jonas JB, Aung T, Bourne RR, Bron AM, Ritch R, Panda-Jonas S. Glaucoma. Lancet. 2017;390(10108):2183–93. 10.1016/S0140-6736(17)31469-1. [DOI] [PubMed] [Google Scholar]
- 2.Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040. Ophthalmology. 2014;121(11):2081–90. 10.1016/j.ophtha.2014.05.013. [DOI] [PubMed] [Google Scholar]
- 3.Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: a review. JAMA. 2014;311(18):1901. 10.1001/jama.2014.3192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Van Eijgen J, Melgarejo JD, Van Laeken J, et al. The relevance of arterial blood pressure in the management of glaucoma progression: a systematic review. Am J Hypertens. 2024;37(3):179–98. 10.1093/ajh/hpad111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Quaranta L, Katsanos A, Russo A, Riva I. 24-hour intraocular pressure and ocular perfusion pressure in glaucoma. Surv Ophthalmol. 2013;58(1):26–41. 10.1016/j.survophthal.2012.05.003. [DOI] [PubMed] [Google Scholar]
- 6.Flammer J. The vascular concept of glaucoma. Surv Ophthalmol. 1994;38:S3–6. 10.1016/0039-6257(94)90041-8. [DOI] [PubMed] [Google Scholar]
- 7.Flammer J, Konieczka K, Flammer AJ. The primary vascular dysregulation syndrome: implications for eye diseases. EPMA Journal. 2013;4(1):14. 10.1186/1878-5085-4-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Choi JA, Lee SN, Jung SH, Won HH, Yun JS. Association of glaucoma and lifestyle with incident cardiovascular disease: a longitudinal prospective study from UK Biobank. Sci Rep. 2023;13(1):2712. 10.1038/s41598-023-29613-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wang M, Chen N, Sun BC, et al. Association between glaucoma and risk of stroke: a systematic review and meta-analysis. Front Neurol. 2022;13:1034976. 10.3389/fneur.2022.1034976. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Lee AJ, Wang JJ, Kifley A, Mitchell P. Open-angle glaucoma and cardiovascular mortality: the Blue Mountains Eye Study. Ophthalmology. 2006;113(7):1069–76. 10.1016/j.ophtha.2006.02.062. [DOI] [PubMed] [Google Scholar]
- 11.Akbari M. The association of primary open-angle glaucoma with mortality: a meta-analysis of observational studies. Arch Ophthalmol. 2009;127(2):204. 10.1001/archophthalmol.2008.571. [DOI] [PubMed] [Google Scholar]
- 12.Klein R. Age-related eye disease and survival: the Beaver Dam eye study. Arch Ophthalmol. 1995;113(3):333. 10.1001/archopht.1995.01100030089026. [DOI] [PubMed] [Google Scholar]
- 13.Nucci C, Martucci A, Cesareo M, et al. Links among glaucoma, neurodegenerative, and vascular diseases of the central nervous system. Prog Brain Res. 2015;221:49–65. 10.1016/bs.pbr.2015.04.010. [DOI] [PubMed] [Google Scholar]
- 14.Hu FB. Prospective study of cataract extraction and risk of coronary heart disease in women. Am J Epidemiol. 2001;153(9):875–81. 10.1093/aje/153.9.875. [DOI] [PubMed] [Google Scholar]
- 15.Klein R, Klein BE, Moss SE, Cruickshanks KJ. Association of ocular disease and mortality in a diabetic population. Arch Ophthalmol. 1999;117(11):1487–95. 10.1001/archopht.117.11.1487. [DOI] [PubMed] [Google Scholar]
- 16.Podgor MJ, Cassel GH, Kannel WB. Lens changes and survival in a population-based study. N Engl J Med. 1985;313(23):1438–44. 10.1056/NEJM198512053132303. [DOI] [PubMed] [Google Scholar]
- 17.West SK. Mixed lens opacities and subsequent mortality. Arch Ophthalmol. 2000;118(3):393. 10.1001/archopht.118.3.393. [DOI] [PubMed] [Google Scholar]
- 18.Hiller R, Podgor MJ, Sperduto RD, Wilson PWF, Chew EY, D’Agostino RB. High intraocular pressure and survival: the Framingham studies. Am J Ophthalmol. 1999;128(4):440–5. 10.1016/S0002-9394(99)00187-7. [DOI] [PubMed] [Google Scholar]
- 19.Egge K, Zahl PH. Survival of glaucoma patients: survival of glaucoma patients. Acta Ophthalmol Scand. 1999;77(4):397–401. 10.1034/j.1600-0420.1999.770407.x. [DOI] [PubMed] [Google Scholar]
- 20.Gini R, Schuemie MJ, Pasqua A, et al. Monitoring compliance with standards of care for chronic diseases using healthcare administrative databases in Italy: strengths and limitations. PLoS ONE. 2017;12(12):e0188377. 10.1371/journal.pone.0188377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kwon J, Jo YH, Jeong D, Shon K, Kook MS. Baseline systolic versus diastolic blood pressure dip and subsequent visual field progression in normal-tension glaucoma. Ophthalmology. 2019;126(7):967–79. 10.1016/j.ophtha.2019.03.001. [DOI] [PubMed] [Google Scholar]
- 22.Wang X, Wang M, Liu H, et al. The association between vascular abnormalities and glaucoma-what comes first? Int J Mol Sci. 2023;24(17):13211. 10.3390/ijms241713211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Leeman M, Kestelyn P. Glaucoma and blood pressure. Hypertension. 2019;73(5):944–50. 10.1161/HYPERTENSIONAHA.118.11507. [DOI] [PubMed] [Google Scholar]
- 24.Li Y, Mitchell W, Elze T, Zebardast N. Association between diabetes, diabetic retinopathy, and glaucoma. Curr Diab Rep. 2021;21(10):38. 10.1007/s11892-021-01404-5. [DOI] [PubMed] [Google Scholar]
- 25.Wong TY, Klein R, Couper DJ, et al. Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study. Lancet. 2001;358(9288):1134–40. 10.1016/S0140-6736(01)06253-5. [DOI] [PubMed] [Google Scholar]
- 26.Konieczka K. Glaucoma patient with suspected Flammer syndrome: diagnostic procedures and therapeutic implications. Klin Monbl Augenheilkd. 2024;241(4):355–60. 10.1055/a-2275-2323. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mitchell P, Smith W, Chey T, Healey PR. Open-angle glaucoma and diabetes: the Blue Mountains eye study, Australia. Ophthalmology. 1997;104(4):712–8. 10.1016/s0161-6420(97)30247-4. [DOI] [PubMed] [Google Scholar]
- 28.Klein BE, Klein R, Jensen SC. Open-angle glaucoma and older-onset diabetes. The Beaver Dam Eye Study. Ophthalmology. 1994;101(7):1173–7. 10.1016/s0161-6420(94)31191-2. [DOI] [PubMed] [Google Scholar]
- 29.Ellis JD, Evans JM, Ruta DA, et al. Glaucoma incidence in an unselected cohort of diabetic patients: is diabetes mellitus a risk factor for glaucoma? DARTS/MEMO collaboration. Diabetes Audit and Research in Tayside Study. Medicines Monitoring Unit. Br J Ophthalmol. 2000;84(11):1218–24. 10.1136/bjo.84.11.1218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Tielsch JM, Katz J, Quigley HA, Javitt JC, Sommer A. Diabetes, intraocular pressure, and primary open-angle glaucoma in the Baltimore Eye Survey. Ophthalmology. 1995;102(1):48–53. 10.1016/s0161-6420(95)31055-x. [DOI] [PubMed] [Google Scholar]
- 31.Yilmaz KC, Sur Gungor S, Ciftci O, Akman A, Muderrisoglu H. Relationship between primary open angle glaucoma and blood pressure. Acta Cardiol. 2020;75(1):54–8. 10.1080/00015385.2018.1549004. [DOI] [PubMed] [Google Scholar]
- 32.Raman P, Suliman NB, Zahari M, Kook M, Ramli N. Low nocturnal diastolic ocular perfusion pressure as a risk factor for NTG progression: a 5-year prospective study. Eye (Lond). 2018;32(7):1183–9. 10.1038/s41433-018-0057-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Bowe A, Grünig M, Schubert J, et al. Circadian variation in arterial blood pressure and glaucomatous optic neuropathy—a systematic review and meta-analysis. Am J Hypertens. 2015;28(9):1077–82. 10.1093/ajh/hpv016. [DOI] [PubMed] [Google Scholar]
- 34.De Moraes CG, Cioffi GA, Weinreb RN, Liebmann JM. New recommendations for the treatment of systemic hypertension and their potential implications for glaucoma management. J Glaucoma. 2018;27(7):567–71. 10.1097/IJG.0000000000000981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Levine RM, Yang A, Brahma V, Martone JF. Management of blood pressure in patients with glaucoma. Curr Cardiol Rep. 2017;19(11):109. 10.1007/s11886-017-0927-x. [DOI] [PubMed] [Google Scholar]
- 36.Karadag R, Koyun E, Ozsoy I, Caliskan M. Evaluation of the 24-hour intraocular pressure and systemic blood pressure at the same time. J Fr Ophtalmol. 2019;42(7):739–45. 10.1016/j.jfo.2019.03.007. [DOI] [PubMed] [Google Scholar]
- 37.Rim TH, Lee SY, Bae HW, Kim SS, Kim CY. Increased stroke risk among patients with open-angle glaucoma: a 10-year follow-up cohort study. Br J Ophthalmol. 2018;102(3):338–43. 10.1136/bjophthalmol-2017-310415. [DOI] [PubMed] [Google Scholar]
- 38.Jung KI, Kim YC, Shin HJ, Park CK. Nationwide cohort study of primary open angle glaucoma risk and cardiovascular factors among in Korean glaucoma suspects. Sci Rep. 2025;15(1):1952. 10.1038/s41598-025-85505-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Wu SY. Open-angle glaucoma and mortality: the Barbados eye studies. Arch Ophthalmol. 2008;126(3):365. 10.1001/archophthalmol.2007.77. [DOI] [PubMed] [Google Scholar]
- 40.Huck A, Harris A, Siesky B, et al. Vascular considerations in glaucoma patients of African and European descent. Acta Ophthalmol. 2014;92(5):e336-340. 10.1111/aos.12354. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data utilized in the study were made available for analysis owing to a collaborative agreement between the Lombardy Region and the Mario Negri Institute aimed at evaluating the appropriateness of drug utilization and analyzing treatment pathways. All data were managed in accordance with current Italian privacy laws.
The datasets analyzed during the current study are not publicly available since they are administrative data property of the Lombardy Region.