Abstract
There may be a connection between glaucoma and cerebrovascular disease (CVD) due to the similarities between the two diseases’ pathophysiologies. We utilised a scoring system based on four factors—current smoking, obesity, regular physical activity, and a healthy diet—to analyse the associations between glaucoma and incident CVD in the prospective UK Biobank cohort, and to determine the extent to which a healthy lifestyle reduced the CVD risk in subjects with glaucoma. Over the course of an average of 8.9 years of follow-up, researchers identified 22,649 cases of incident CVD (4.9%). In multivariate Cox regression analyses, subjects with glaucoma were found to have a higher risk of incident CVD than controls (hazard ratio [HR]:1.19, 95% confidence interval [CI] 1.03-1.37; p = 0.016). Further subgroup analyses showed that glaucoma increased incident CVD risk in both younger (40-55 years) and older (56-70 years) and both sexes, with a higher risk in younger (HR: 1.33, CI 1.02-1.74) and female subjects (HR: 1.32, CI 1.14–1.52). The highest absolute risks were seen in those who not only had glaucoma but also led an unhealthy lifestyle, as determined by our analysis of the correlations between glaucoma and incident CVD by lifestyle factors (HR: 2.66, CI 2.22–3.19). Finally, glaucoma was found to be a significant predictor of incident CVD. Adults with glaucoma who lead a healthy lifestyle have a much lower risk of developing cardiovascular disease.
Introduction
When it comes to irreversible blindness, glaucoma is by far the most common cause worldwide. From 1990 to 2020, the prevalence of glaucoma increased by 10.7%; it remains a leading cause of vision impairment, especially among the elderly2. Many systemic vascular comorbidities3,4,5,6,7,8,9,10,11,12 have been linked to glaucoma, according to a growing body of literature. With rising global average ages and life expectancies, glaucoma’s visual impairment imposes substantial social burdens. Pathogenesis of glaucoma includes vascular involvement, which dysregulates ocular perfusion and mechanical stress due to elevated intraocular pressure (IOP). Disrupted autoregulation, endothelial dysfunction, and inadequate ocular blood flow due to atherosclerosis3–6,7,8–10,11 all contribute to the underlying mechanism of vascular dysregulation. Numerous studies have found that glaucoma patients have both systemic circulatory abnormalities and local vascular abnormalities in the region of the optic nerve head11,13.
Worldwide, cardiovascular disease (CVD) kills more people than any other disease. There may be a link between cardiovascular disease and glaucoma because the two conditions share similar subclinical pathophysiological features. Patients with glaucoma are at increased risk of dying from cardiovascular disease compared to the general population14. The prevalence of cardiovascular disease and other comorbidities is significantly higher in patients with glaucoma compared to age- and sex-matched controls15. Both genetic and lifestyle factors contribute to the prevalence of CVD. Increases in blood pressure, glucose, and lipids, as well as the prevalence of overweight or obesity, are all linked to unhealthy lifestyle habits16; in contrast, adopting a heart-healthy lifestyle significantly lowers the risk of incident cardiovascular events17,18. When compared to those who live an unhealthy lifestyle, those who adopt such a routine reduce their risk of developing ischaemic heart disease by half19. To this end, it is crucial to determine what aspects of one’s lifestyle may increase one’s likelihood of developing cardiovascular disease. In this study, we primarily focused on determining, in a sizable prospective UK Biobank cohort, whether or not glaucoma is associated with an increased incidence of CVD. We also broke down our findings by age, sex, and intraocular pressure to get a clearer picture of how glaucoma relates to cardiovascular disease. The association between a healthy lifestyle and a decreased risk of CVD in glaucoma participants was then evaluated.
Methods
The data from the UK Biobank cohort was used for this study. About half a million UK residents were enrolled in this prospective cohort study between 2006 and 2010 (those enrolled were between the ages of 40 and 69), and their health-related outcomes were tracked for a total of 20. UK Biobank’s protocol is freely available online, and the project’s architecture and data collection methods have been described21. At the time of enrollment, all participants completed a baseline survey via an electronic questionnaire or an interview with a trained nurse to provide extensive phenotypic data on demographics, lifestyle behaviours, and medical histories. Blood and urine samples were taken, along with a variety of other physical measurements. To link the UK Biobank dataset with secondary data sources such as primary care records, hospital inpatient records, and cancer and death registry data22, all participants provided written informed consent. We ruled out 35,799 people who had a history of cardiovascular disease (CAD), heart attack (MI), or stroke (IA) before the index date. The Northwest Multi-center Research Ethics Committee gave their stamp of approval to this study on June 17, 2011 (reference 11/NW/0382), and again on May 13, 2016 (reference 16/NW/0274). The research followed all applicable principles of the Declaration of Helsinki. The utilisation of the UK Biobank was authorised (application no. 90981).
De Novo Cardiovascular Disease was the Primary Outcome (CAD, MI, and ischemic stroke). Hospitalization and disease onset records were used to establish the criteria for CAD diagnosis. UK Biobank23 used algorithms to define MI and ischemic stroke. Limiting our analysis to only the first CVD outcome allowed us to rule out the possibility of multiple events. On the first examination of the database24, glaucoma was considered present if the patient reported having glaucoma on the questionnaire or during the interview, or if the diagnostic code for glaucoma (H40) was present, but not secondary glaucoma (H40.3, H40.4, H40.5, H40.6, and H42). In Supplemental Table S1, we give in-depth descriptions of both glaucoma and CVD. 103,143 people had their IOPs checked at their respective testing facilities. The Ocular Response Analyzer was used to take a single reading of the intraocular pressure in each eye (Reichert Corp., Philadelphia, PA). As a linear combination of the inward and outward applanation tensions25, we calculated the corneal compensated IOP. Right and left eye intraocular pressure readings were averaged. If only one eye’s worth of information was available, that eye’s intraocular pressure was used.
Age, sex, ethnicity, family medical history, medications, and lifestyle factors were among the first pieces of information gathered. Using a HEM-70151 T digital blood pressure monitor, the subjects’ blood pressure was determined (Omron, Hoofddorp, The Netherlands). The BV-418 MA body composition analyzer was used to measure weight, and a Seca 202 height measure (Seca, Birmingham, UK) was used to measure height (Tanita, Arlington Heights, IL). Dyslipidemia and hypertension are defined at the beginning of the study in Supplemental Table S1. Obesity, current smoking, regular physical activity, and dietary habits19,26 were evaluated in accordance with American Heart Association guidelines because of their significant effects on cardiovascular health. According to the international criteria developed by the World Health Organization, obesity is defined as a body mass index (BMI) greater than 30 kg/m2. Every individual was categorised as either a “current smoker” or “non-smoker” (not current and never-smokers). At the start, participants were asked to keep track of their own physical activity levels. Moderate activity on more than 5 days per week or vigorous activity on more than 3 days per week was considered regular physical activity. Fruits, vegetables, whole grains, fish, dairy products, refined grains, processed meats, unprocessed meats, and sugar-sweetened beverages all factored into the definition of healthy eating habits based on guidelines for cardiovascular health. By the standards of the food frequency questionnaire27, a diet was considered healthy if at least half of the recommendations were met by the participants. Those actions were classified as either unhealthy (with 0–1 healthy factors), intermediate (2–3 healthy factors), or healthy (with 3 healthy factors). Supplemental Table S2 displays the data.
A blood sample was taken and processed according to established protocols at the start of the evaluation. It has already been described28 how to collect and analyse a urine or blood sample. Hemoglobin A1c was measured with a hexokinase assay and low-density lipoprotein (LDL) cholesterol was measured with an enzymatic selective protection assay on high-performance liquid chromatography analyzers manufactured by Bio-Rad (Hercules, CA) and Beckman (Brea, CA) respectively. Additional Figure S1 details the occurrence of missing values. Please visit https://www.ukbiobank.ac.uk/ for further information.
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To compare the participants’ pre-study clinical characteristics, we used the independent t-test for continuous variables and the chi-square test for categorical variables. Rates of cardiovascular disease incidents per one thousand lives lived are shown. We employed multivariable Cox regression analysis to assess the link between glaucoma and CVD. Age, sex, race, and body mass index were adjusted in Model 1; a family history of cardiovascular disease, body mass index, smoking, and eating habits were adjusted in Model 2; age, sex, and race were also adjusted in Model 3; blood pressure, estimated glomerular filtration rate, haemoglobin A1c, and low-density lipoprotein cholesterol levels were adjusted in Model 4; and finally, aspirin use, any anti-hypertensive agent, and any lipid Cox’s models were divided into two categories based on age ( 55 and > 55) and gender. We evaluated the multiplicative interactions between glaucoma and lifestyle behaviours in terms of the risk for CVD to determine if these factors moderated the association between glaucoma and CVD. In addition, we analysed CVD rates by IOP decile. The follow-up period began on the date of enrollment and ended on the earlier of the date of loss to follow-up, the date of the scheduled end of the follow-up period (January 31, 2018 in England and Wales and November 30, 2016 in Scotland), or the date of death. Participants who were missing necessary data were not included. Examining the proportional hazard assumptions was done with Schoenfeld residuals and log minus log plots. All tests were two-tailed, and a value of P less than 0.05 was considered significant. R 3.9.0 was used for all statistical testing.
Results
Supplemental Fig. S1 displays the sample sizes for eligible UK Biobank participants with complete data for our various analyses. During a median follow-up period of 8.9 years, 22,649 incident cases of CVD were recorded (4.9% of the total population). There were 5.61 cases of CVD for every 1,000 people aged 18 and older (95% CI, 5.53-5.68). Table 1 lists the descriptive baseline characteristics of the 466,706 participants who met the inclusion criteria. The prevalence of hypertension, dyslipidemia, cancer, and risky lifestyle choices were all higher in glaucoma patients compared to those without the disease (Table 1). Table 2 summarises the incident CVD HRs from the univariate and multivariate Cox’s regression analyses. When glaucoma patients were compared to healthy controls, they had a 1.60 higher risk of cardiovascular disease (HR 1.48-1.74, P 0.001). Participants with glaucoma had a significantly higher risk of developing incident CVD after adjusting for family history, lifestyle factors, biometric measures, and medications (Model 4, adjusted HR 1.19, 95% CI 1.03-1.37, P = 0.016). Subgroup analyses showed that the risk of incident CVD was higher in both younger (40-55 years) and older (56-70 years) individuals with glaucoma (HR: 1.33, CI 1.02-1.74), and female (HR: 1.32, CI 1.14-1.52) subjects (Supplemental Table S3). The links between glaucoma and incident CVD due to lifestyle factors are depicted in Figure 1. Unhealthy lifestyle was a strong predictor of CVD in both the glaucoma and control groups; non-glaucomatous individuals with unhealthy lifestyles had an increased risk for CVD (absolute risk 7.17%, adjusted HR 2.01, 95% CI 1.93-2.08, P 0.001), and those with glaucoma and unhealthy lifestyles had the highest absolute risks (absolute risk 12.24%, adjusted HR 2.66, 95% CI 2.22-3.
