This cohort study investigates the association of blood pressure with rates of change in the macular ganglion cell complex among patients with glaucoma.
Key Points
Question
Is baseline blood pressure associated with rates of macular ganglion cell complex (GCC) thinning in glaucoma?
Findings
In this cohort study of 105 eyes from 105 patients with moderate to advanced glaucoma, lower baseline diastolic blood pressure was associated with faster rates of GCC thinning. This association was more pronounced at higher intraocular pressure levels.
Meaning
The findings suggest that evaluating and addressing diastolic blood pressure may be considered as a therapeutic measure in patients with glaucoma.
Abstract
Importance
There are scarce data on the association of blood pressure measures with subsequent macular structural rates of change in patients with glaucoma.
Objective
To investigate the association of baseline blood pressure measures with rates of change of the macular ganglion cell complex in patients with central or moderate to advanced glaucoma damage at baseline.
Design, Setting, and Participants
This prospective cohort study, conducted from August 2021 to August 2022, used data from patients in the Advanced Glaucoma Progression Study at the University of California, Los Angeles. Participants were between 39 and 80 years of age and had more than 4 macular imaging tests and 2 or more years of follow-up.
Exposures
A diagnosis of glaucoma with either central damage or a visual field mean deviation worse than −6 dB.
Main Outcomes and Measures
The main outcome was the association of blood pressure measures with ganglion cell complex rates of change. Macular ganglion cell complex thickness rates of change were estimated with a bayesian hierarchical model. This model included relevant demographic and clinical factors. Blood pressure measures, intraocular pressure, and their interactions were added to the model to assess the association of baseline blood pressure measures with global ganglion cell complex rates of change.
Results
The cohort included 105 eyes from 105 participants. The mean (SD) age, 10-2 visual field mean deviation, and follow-up time were 66.9 (8.5) years, –8.3 (5.3) dB, and 3.6 (0.4) years, respectively, and 67 patients (63.8%) were female. The racial and ethnic makeup of the cohort was 15 African American (14.3%), 23 Asian (21.9%), 12 Hispanic (11.4%), and 55 White (52.4%) individuals based on patient self-report. In multivariable analyses, female sex, history of taking blood pressure medications, higher intraocular pressure, thicker central corneal thickness, shorter axial length, higher contrast sensitivity at 12 cycles per degree, and higher baseline 10-2 visual field mean deviation were associated with faster ganglion cell complex thinning. Lower diastolic blood pressure was associated with faster rates of ganglion cell complex thinning at higher intraocular pressures. For intraocular pressures of 8 and of 16 mm Hg (10% and 90% quantiles, respectively), every 10 mm Hg–lower increment of diastolic blood pressure was associated with 0.011 μm/y slower and –0.130 μm/y faster rates of ganglion cell complex thinning, respectively.
Conclusions and Relevance
In this cohort study, a combination of lower diastolic blood pressure and higher intraocular pressure at baseline was associated with faster rates of ganglion cell complex thinning. These findings support consideration of evaluating and addressing diastolic blood pressure as a therapeutic measure in patients with glaucoma if supported by appropriate clinical trials.
Introduction
Glaucoma is a progressive optic neuropathy and is a leading cause of irreversible blindness worldwide.1,2 Several risk factors have been associated with the presence of glaucoma or glaucoma progression.3,4,5 Among those, intraocular pressure (IOP) is a proven and modifiable risk factor for glaucoma.6,7,8,9,10 A few studies have shown the association of decreased blood flow with the pathogenesis of glaucoma.11,12,13 Ocular perfusion pressure (OPP) has been identified as a biomarker for glaucoma. Although perfusion pressure has been reported as a risk factor for development of glaucoma or its progression in many prognostic studies in the context of multiple regression analyses, this approach is flawed statistically due to the inseparability of blood pressure (BP) and IOP in the determination of perfusion pressure.14,15,16,17,18,19,20 Few studies have evaluated the association of baseline BP measurements with longitudinal changes in structure or function in patients with glaucoma.21,22,23
We recently demonstrated that ganglion cell complex (GCC) thickness had the largest signal among macular structural measures for detection of glaucoma progression in eyes with central or moderate to severe glaucoma damage at baseline.24,25 Our recently developed longitudinal bayesian framework enables us to properly estimate global and local structural rates of change in the macular region and provides an opportunity to construct and explore prognostic models based on structural change outcomes.26
The purpose of the current study was to investigate the association of baseline BP measures with longitudinal changes of macular GCC within our bayesian hierarchical model. To this aim, we explored the association of BP with macular rates of change after adjusting for relevant demographic and clinical factors, including IOP.
Methods
Patients in the Advanced Glaucoma Progression Study (AGPS), an ongoing, prospective, longitudinal cohort study at the University of California, Los Angeles (UCLA), were enrolled in this cohort study. UCLA’s institutional review board approved this study, and the study adhered to the Health Insurance Portability and Accountability Act policies. All patients provided written informed consent at the time of enrollment in the study. The study participants did not receive any stipend or other incentive. Data collection for the cohort started in June 2012, and analyses are based on the data aggregated in June 2018. This study was designed in August 2021 and finalized in August 2022. The findings were reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Inclusion criteria in the AGPS were (1) diagnosis of open-angle or angle-closure glaucoma and (2) evidence of either central damage on 24-2 visual fields, defined as 2 or more points within the central 10° with P < .05 on the pattern deviation plot, or visual field mean deviation (MD) worse than −6 dB. Exclusion criteria were baseline age younger than 39 years or older than 80 years, best-corrected visual acuity less than 20/50, refractive error exceeding 8 diopters of sphere or 3 diopters of cylinder, any retinal or neurological disease affecting optical coherence tomography (OCT) measurements, or any ocular pathology (except cataract). Study eyes underwent clinical examinations and macular OCT imaging approximately every 6 months. Based on the aforementioned criteria, 1 eye of each patient was considered as the index eye and was included in this prospective study. In case both eyes met inclusion criteria, the eye with worse MD was considered as the study eye.
Baseline demographic and clinical factors included in the model were sex, race and ethnicity (African American, Asian, Hispanic, or White), age at baseline, central corneal thickness, axial length, presence of diabetes or hypertension, history of BP medication use, IOP (measured by Goldmann applanation tonometry), contrast sensitivity, 10-2 visual field MD, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Patient data were collected prospectively. Race and ethnicity were self-reported at the time of enrollment and were included because of variation of the pathophysiology and course of glaucoma between different races and ethnicities.
Blood pressure measurements were obtained by a trained research coordinator. An Omron BP monitor (model BP760N) was used for measuring BP. Study participants were seated quietly for at least 5 minutes before BP measurement. The right arm was preferred for consistency and comparison with the standard tables. The cuff was inflated to 30 mm Hg above palpated SBP and deflated at a rate of 2 to 3 mm Hg per second.
Macular OCT Imaging
The Spectralis spectral-domain OCT (Heidelberg Engineering) was used to acquire macular OCT volume scans. The posterior pole algorithm performs 30° × 25° volume scans of the macula centered on the fovea, consisting of 61 B-scans spaced approximately 120 μm apart. Spectralis Glaucoma Module Premium Edition software (Heidelberg Engineering) was used to automatically segment individual retinal layers before data export. Images were reviewed for segmentation errors and image artifacts. Segmentation errors were manually corrected with the device’s built-in software. A low-quality B-scan image was defined as a quality factor less than 15, more than 10% missing data, inadequate segmentation, or presence of any artifacts, such as mirror artifacts. The individual layer thickness measurements are provided by the device as 8 × 8 arrays of 3° × 3° superpixels for the central 24° × 24° region centered on the fovea. We excluded the bottom row and the nasal-most column of the macular grid due to the high observed noise in these 2 regions and analyzed a 7 × 7 grid consisting of 49 superpixels.27,28 Ganglion cell complex thickness was calculated by summing the retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thickness measurements.
Statistical Analysis
Our basic bayesian hierarchical model and data analysis without covariates were described previously.26 We screened data for outliers and removed approximately 0.5% of observations. We inspected profile plots and summary plots29 and plotted univariate distributions of covariates.
For each individual and superpixel, the model has an individual-superpixel intercept, slope, and residual variance. Each superpixel has 7 superpixel-level population parameters: population intercept, population slope, variance of the random intercepts, correlation between random intercepts and slopes, variance of random slopes, and mean and SD of the (log) residual variances. We set normal priors for transformations of these parameters across superpixels with unknown macula-wide global means and variances. Full details of statistical methods are provided in the eMethods in Supplement 1.
To simplify prior specification for covariate coefficients, we standardized each covariate by subtracting the covariate mean and then dividing by the covariate SD. We used a horseshoe prior30,31 for all coefficients. We fit models with a single covariate, including the covariate main effect and covariate-by-time interaction. The covariate main effect modeled the association of covariates with the GCC baseline value while the covariate-by-time interaction modeled the association of covariates with rate of change. We then fit multivariable models with all covariate main effects and covariate-by-time interactions. We fit a final model that included IOP and DBP and their interaction IOP × DBP, with the interaction having an association with the baseline value and with the rate of change. The same final model was repeated for SBP separately instead of DBP.
We reported posterior means, SDs, and 95% credible intervals of coefficients of the untransformed covariates and the posterior probability that the coefficient was of the sign opposite the sign of the posterior mean given the data. This is the P value reported in tables (P < .025 was considered significant). The P value can be interpreted as a 1-sided P value for the null hypothesis that the coefficient was 0 and also as the posterior probability that the unknown coefficient was positive (or negative). We identified a coefficient as significantly positive (or negative) if the posterior probability that it was positive (or negative) was greater than 0.975. In the model with DBP and IOP main effects and DBP × IOP interaction, the coefficient of DBP was βDBP + IOP * βDBP*IOP and the effect of DBP depended on the value of IOP. The same applies for the model incorporating SBP and the interaction with IOP. We described the association of DBP (or SBP) and IOP with the rate of thinning of GCC (μm/y) over time with other variables held at their mean or reference group. Statistical analyses were conducted using R, version 4.0.4 (R Project for Statistical Computing).
Results
Of a total of 111 patients from the AGPS, 4 study participants had missing values for 1 of the factors and were removed from any model including those covariates. Additionally, 2 study participants had outlying values of DBP and were removed from all reported analyses, leaving 105 study participants in the multivariable analyses. Table 1 presents summaries of the baseline demographic and clinical characteristics of the 105 participants (mean [SD] age at baseline, 66.9 [8.5] years; 67 [63.8%] female and 38 [36.2%] male; 15 [14.3%] African American, 23 [21.9%] Asian, 12 [11.4%] Hispanic, and 55 [52.4%] White). The mean (SD) follow-up time and number of visits were 3.6 (0.4) years and 7.4 (1.1) visits, respectively. The mean (SD) 10-2 visual field MD and IOP were −8.3 (5.3) dB and 12.5 (3.6) mm Hg, respectively. The mean (SD) baseline DBP and SBP were 81.9 (9.3) mm Hg and 136.2 (18.7) mm Hg, respectively. Among all the study participants, 12 eyes (11.4%) underwent trabeculectomy, 2 eyes (1.9%) had a combined phaco-trabeculectomy, 6 eyes (5.7%) required trabeculectomy revision, 1 eye (1.0%) underwent placement of an Ahmed Glaucoma Valve, and 1 eye (1.0%) received an iStent surgery during the follow-up period.
Table 1. Demographic and Clinical Characteristics of Study Participants.
| Variable | Participants (N = 105)a |
|---|---|
| Age at baseline, mean (SD), y | 66.9 (8.5) |
| Sex | |
| Female | 67 (63.8) |
| Male | 38 (36.2) |
| Race and ethnicity | |
| African American | 15 (14.3) |
| Asian | 23 (21.9) |
| Hispanic | 12 (11.4) |
| White | 55 (52.4) |
| Blood pressure, mean (SD), mm Hg | |
| Diastolic | 81.9 (9.3) |
| Systolic | 136.2 (18.7) |
| History of blood pressure medication use | 35 (33.3) |
| Hypertension | 37 (35.2) |
| Diabetes | 17 (16.2) |
| Intraocular pressure, mean (SD), mm Hg | 12.5 (3.6) |
| Central corneal thickness, mean (SD), μm | 534.7 (38.5) |
| Axial length, mean (SD), mm | 24.5 (1.5) |
| Pseudophakia | 42 (40.0) |
| Contrast sensitivity at 12 cycles per degree, mean (SD), log units | 4.1 (1.8) |
| 10-2 Visual field mean deviation, mean (SD), dB | –8.3 (5.3) |
Data are presented as the number (percentage) of participants unless otherwise indicated.
Table 2 provides summaries of the GCC slope coefficient posteriors in the univariable models. We found that female sex, African American race, history of BP medication use, higher IOP, thicker central corneal thickness, shorter axial length, higher contrast sensitivity at 12 cycles per degree, and greater (better) 10-2 visual field MD were associated with faster (worse) rates of GCC thinning over time. Diastolic BP was significantly associated with faster rates of GCC thinning (−0.061 μm/y per 10–mm Hg decrease in DBP; P < .001). The association of factors with the covariate intercepts in the cohort is presented in eTable 1 in Supplement 1.
Table 2. Association of Individual Covariates With Ganglion Cell Complex Rates of Change in Univariable Models.
| Variable | Slope | |
|---|---|---|
| Posterior, mean (95% CrI) | P value | |
| Age at baseline, per 10 y | –0.018 (–0.050 to 0.010) | .12 |
| Female sex | –0.131 (–0.188 to –0.073) | <.001 |
| Race and ethnicity | ||
| African American | 0.256 (0.172 to 0.338) | <.001 |
| Asian | 0.003 (–0.056 to 0.063) | .46 |
| Hispanic | –0.034 (–0.118 to 0.037) | .20 |
| White | 1 [Reference] | |
| Blood pressure, per 10 mm Hg | ||
| Diastolic | 0.061 (0.033 to 0.090) | <.001 |
| Systolic | 0.004 (–0.007 to 0.018) | .28 |
| History of blood pressure medication use | –0.118 (–0.174 to –0.061) | <.001 |
| Hypertension | –0.013 (–0.064 to 0.028) | .29 |
| Diabetes | –0.046 (–0.121 to 0.020) | .10 |
| Intraocular pressure, per 1 mm Hg | –0.020 (–0.027 to –0.013) | <.001 |
| Central corneal thickness, per 10 μm | –0.035 (–0.041 to –0.028) | <.001 |
| Axial length, per 1 mm | 0.048 (0.030 to 0.066) | <.001 |
| Contrast sensitivity at 12 cycles per degree, per log unit | –0.018 (–0.034 to –0.003) | .008 |
| 10-2 Visual field mean deviation, per 1 dB | –0.010 (–0.015 to –0.005) | <.001 |
Abbreviation: CrI, credible interval.
Results from the multivariable models are provided in Table 3. The interaction DBP × IOP had a significant association with the rate of GCC thinning; eyes with higher DBP, lower IOP, or both had similar negative slopes. Based on this model, female sex, history of taking blood pressure medications, higher intraocular pressure, thicker central corneal thickness, shorter axial length, higher contrast sensitivity at 12 cycle per degree, and higher baseline 10-2 visual field mean deviation were associated with faster ganglion cell complex thinning. Eyes of participants with DBP greater than 80 mm Hg or with IOP less than 10 mm Hg had fitted rates of thinning better than −0.4 μm/y with other variables held at their mean or reference levels. Eyes of participants with low DBP and high IOP had faster (more negative) GCC slopes ranging from −0.8 μm/y (IOP, 20 mm Hg; DBP, 60 mm Hg) to −0.4 μm/y (IOP, 15 mm Hg; DBP, 80 mm Hg). For example, at an IOP of 8 mm Hg (10th percentile) to 16 mm Hg (90th percentile), every 10–mm Hg decrease in DBP was associated with a slower rate of GCC thinning (0.011 μm/y; faster, –0.130 μm/y). Figure 1 shows fitted lines for hypothetical study participants at 4 combinations of 10th or 90th percentiles of DBP (70 mm Hg or 94 mm Hg, respectively) and IOP (8 mm Hg or 16 mm Hg, respectively), with other variables held at their mean or reference level. The combination of the 10th percentile DBP and the 90th percentile IOP led to the fastest rate of GCC thinning. Figure 2 shows a heat map of GCC slopes for values of DBP and IOP in the ranges of 60 to 100 mm Hg and 5 to 30 mm Hg, respectively. The lowest DBPs and highest IOPs were associated with the most negative GCC slopes. The association of variables with the GCC population intercept for the multivariable model is presented in eTable 2 in Supplement 1. Results of multivariable models including the SBP main effect and its interaction with IOP are provided in eTables 3 and 4 in Supplement 1. We carried out the univariable and multivariable models on the subset of study participants with open-angle glaucoma only (n = 98), and the results were found to be similar to those of the entire group (eResults and eTables 5-8 in Supplement 1).
Table 3. Final Multivariable Model Including Diastolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Associations With the Rates of Change of Ganglion Cell Complex.
| Variable | Slope | |
|---|---|---|
| Posterior, mean (95% CrI) | P value | |
| Age at baseline, per 10 y | –0.012 (–0.044 to 0.014) | .22 |
| Female sex | –0.106 (–0.170 to –0.042) | .001 |
| Race and ethnicity | ||
| African American | 0.315 (0.227 to 0.405) | <.001 |
| Asian | –0.012 (–0.075 to 0.042) | .32 |
| Hispanic | –0.077 (–0.176 to 0.009) | .05 |
| White | 1 [Reference] | |
| Hypertension | 0.070 (–0.001 to 0.145) | .03 |
| Diabetes | –0.033 (–0.108 to 0.025) | .17 |
| Central corneal thickness, per 10 μm | –0.034 (–0.041 to –0.026) | <.001 |
| Axial length, per 1 mm | 0.026 (0.005 to 0.046) | .006 |
| Contrast sensitivity at 12 cycles per degree, per log unit | –0.025 (–0.042 to –0.008) | .002 |
| 10-2 Visual field mean deviation, per 1 dB | –0.008 (–0.014 to –0.003) | .003 |
| History of blood pressure medication use | –0.245 (–0.317 to –0.171) | <.001 |
| Intraocular pressure, per 1 mm Hg | –0.155 (–0.225 to –0.085) | <.001 |
| Diastolic blood pressure, per 10 mm Hg | –0.153 (–0.256 to –0.051) | .003 |
| DBP/10 × IOP interaction | 0.018 (0.009 to 0.026) | <.001 |
Abbreviation: CrI, credible interval.
Figure 1. Posterior Mean Ganglion Cell Complex (GCC) Thickness Over Time for 4 Combinations of Low or High Diastolic Blood Pressure (DBP) and Low or High Intraocular Pressure (IOP).
Low DBP was 70 mm Hg (10% quantile) and high DBP, 94 mm Hg (90% quantile); low IOP was 16 mm Hg (90% quantile) and high IOP, 8 mm Hg (10% quantile). Other continuous covariates were set to their mean values, and categorical covariates were set to their reference categories.
Figure 2. Heat Map of Posterior Mean Ganglion Cell Complex Slope as a Function of Diastolic Blood Pressure (DBP) and Intraocular Pressure (IOP) With Other Continuous Covariates Set to Their Mean Values and Categorical Covariates Set to Their Reference Categories.
Dots indicate the unique values of IOP and DBP.
Discussion
We explored the association of baseline BP measures with subsequent macular structural rates of change in a cohort of eyes with central or moderate to advanced glaucoma damage at baseline. In the final multivariable model, after adjusting for confounding factors and including an interaction between BP and IOP, lower DBP with higher IOP was associated with faster rates of change of GCC thinning.
Studies have shown that lower OPP is a risk factor for glaucoma progression.14,15,16,17,18 Ocular perfusion pressure is the mean arterial pressure minus the IOP. Therefore, the IOP, the mean arterial pressure, or both affect it. Both the Barbados Eye Study5 and the Rotterdam Study32 reported that lower diastolic perfusion pressure was associated with a 3-fold increase in the risk of development of glaucoma. Ocular perfusion pressure explores 1 linear combination of BP and IOP. Considering only OPP or mean arterial perfusion pressure as factors associated with the GCC rate of change implies that an increase in BP and a decrease in IOP trade off on a 1-to-1 basis for estimating the GCC rate of decrease. In contrast, our model included separate coefficients for DBP, IOP, and the DBP × IOP interaction; we found that the level of IOP modified the association of DBP with change in GCC thinning, and similarly, the level of DBP modified the association of IOP with change in GCC thinning.
We constructed a multivariable hierarchical bayesian model to investigate the association between BP parameters and rates of GCC change while adjusting for IOP and other known confounding factors. Based on existing literature and initial explorations of the model, an interaction between DBP and IOP seemed plausible. In the final model, changes in DBP (IOP) were associated with the level of IOP (DBP), with higher IOP and lower DBP associated with the fastest loss of GCC thickness. Similar results were found when the analyses were carried out on patients with only open-angle glaucoma. These results support the idea of an intricate interaction between DBP and IOP and suggest that using perfusion pressure as a prognostic factor is likely suboptimal.
There is extensive evidence in the literature supporting the association of vascular risk factors and blood flow with the pathogenesis of glaucoma.11,12,13 This may be particularly important in patients with normal-tension glaucoma (NTG). Various studies have supported the importance of vascular risk factors for NTG pathogenesis.23,33,34 The Collaborative Normal-Tension Glaucoma Study found that vascular risk factors, such as migraine and disc hemorrhage, were associated with glaucoma progression.34 The Low-Pressure Glaucoma Treatment Study reported that use of antisystemic hypertension medications was a risk factor for visual field progression.33 Lee et al23 recently found that lower SBP and DBP were associated with progressive thinning of the circumpapillary retinal nerve fiber layer and macular ganglion cell–inner plexiform layer in a longitudinal series of NTG eyes. Based on the evidence in the literature and results of our study, avoiding overtreatment of systemic hypertension seems to be a sensible choice in patients with glaucoma.
There are several ways in which BP affects the optic nerve in glaucoma. Both high and low BP could result in changes in the blood vessels, which could influence blood flow to the optic nerve. Low BP can cause compensatory vasoconstriction in almost all organs of the body, which could further reduce blood flow.35 On the other hand, high BP is a risk factor for atherosclerosis, which could, over time, chronically decrease blood flow to the optic nerve.12 The reduced blood flow could lead to retinal ganglion cell damage and subsequent visual field loss.36 Pappelis and Jansonius37 recently reported that both lower- and higher-than-normal BPs and ineffective autoregulation were associated with thinner ganglion cell–inner plexiform layer measurements in healthy individuals.
Systemic hypertension is a known risk factor for cardiovascular diseases.38 A report from the American College of Cardiology and American Heart Association stated that 46% of the US population has hypertension and that at least one-third are taking 1 or more antihypertensive treatments.39 Although the results of our study suggest that addressing low BP in patients with glaucoma treated with antihypertensive medications may be indicated, a multidisciplinary approach is needed to balance slowing the course of glaucoma and preventing cardiovascular diseases. Some studies have demonstrated that higher DBP, after adjusting for SBP, may not be a risk factor for cardiovascular diseases.40,41 As a lower DBP, but not SBP, was found to be a factor associated with progressive GCC thinning in our study, avoiding low DBP may be considered as a safe therapeutic approach for slowing the course of glaucoma progression.
Our model analyzes GCC measurements on a 7 × 7 grid of superpixels over time in multiple participants. This model allows estimation of population distributions of individual participant parameters for improved precision over simple linear regression analyzing data from a single patient in 1 superpixel over time. Benefits include more efficient estimation of rates of change. The current model only allowed for a single macular-wide association of covariates with GCC change rates and did not allow for covariate effects to vary across superpixels. Our model is being refined continuously, and we expect future versions to improve on the current model.
Our findings also have other clinical and research implications. Changes in structural measures are easy to detect, especially in eyes with moderate to advanced glaucoma as visual field fluctuations may be large in these eyes.28 Prognostic models based on structural progression may thus be more efficient, decreasing the need for longer follow-up of patients in research settings. Such prognostic models may also be used to identify new biomarkers for glaucoma progression or to confirm such biomarkers detected in prognostic models based on functional progression.
Limitations
This study has limitations. It was an observational study; differences that were found were differences across study participants, and we did not explore the effects of modifying BP in a patient longitudinally. The effect of BP treatment and the influence of glaucoma treatment are subjects of further research that we plan to address in the next stage of this work. Another limitation of this study was that BP measurements were obtained during clinic hours, and therefore, the findings of our study cannot address the potential influence of nocturnal BP decreases. A nocturnal BP decrease occurs in some patients with glaucoma and may be associated with higher risk of glaucoma progression.42,43 Older persons may have hypertension associated with anxiety during an office visit and excessive variability in SBP.44 It has been reported that automated BP measurements in a quiet room, as performed in our study, provide more accurate measurements than manual measurements and may minimize an anxiety-related BP rise.45
Conclusions
This study found that lower DBP with higher IOP was associated with faster rates of macular GCC thinning in patients with glaucoma and central or moderate to advanced damage at baseline. Therefore, evaluating and addressing DBP could be considered as a therapeutic measure in patients with glaucoma if supported by appropriate clinical trials. Cardiovascular risk assessment and treatment of patients with glaucoma in conjunction with their internist may be indicated to optimize BP in these patients.
eMethods. Bayesian Hierarchical Longitudinal Model With Random Intercepts, Slopes, and Residual Variances
eTable 1. Association of Individual Covariates With Ganglion Cell Complex Baseline Thickness in Univariable Prognostic Models
eTable 2. Final Multivariable Model Including Diastolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on Ganglion Cell Complex Baseline Thickness
eTable 3. Multivariable Model Including Systolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on Ganglion Cell Complex Baseline Thickness
eTable 4. Multivariable Model Including Systolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariates on the Rates of Change of Ganglion Cell Complex
eResults. Univariable and Multivariable Bayesian Hierarchical Model for the Subset of Eyes With Open-Angle Glaucoma
eTable 5. Association of Individual Covariates With Ganglion Cell Complex Rates of Change in Univariable Prognostic Models for the Subset of Eyes With Open-angle Glaucoma
eTable 6. Association of Individual Covariates With Ganglion Cell Complex Intercepts in Univariable Prognostic Models for the Subset of Eyes With Open-angle Glaucoma
eTable 7. Final Multivariable Model Including Diastolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on the Rates of Change of Ganglion Cell Complex for the Subset of Eyes With Open-angle Glaucoma
eTable 8. Final Multivariable Model Including Diastolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on the Intercepts of Ganglion Cell Complex
eReferences
Data Sharing Statement
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods. Bayesian Hierarchical Longitudinal Model With Random Intercepts, Slopes, and Residual Variances
eTable 1. Association of Individual Covariates With Ganglion Cell Complex Baseline Thickness in Univariable Prognostic Models
eTable 2. Final Multivariable Model Including Diastolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on Ganglion Cell Complex Baseline Thickness
eTable 3. Multivariable Model Including Systolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on Ganglion Cell Complex Baseline Thickness
eTable 4. Multivariable Model Including Systolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariates on the Rates of Change of Ganglion Cell Complex
eResults. Univariable and Multivariable Bayesian Hierarchical Model for the Subset of Eyes With Open-Angle Glaucoma
eTable 5. Association of Individual Covariates With Ganglion Cell Complex Rates of Change in Univariable Prognostic Models for the Subset of Eyes With Open-angle Glaucoma
eTable 6. Association of Individual Covariates With Ganglion Cell Complex Intercepts in Univariable Prognostic Models for the Subset of Eyes With Open-angle Glaucoma
eTable 7. Final Multivariable Model Including Diastolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on the Rates of Change of Ganglion Cell Complex for the Subset of Eyes With Open-angle Glaucoma
eTable 8. Final Multivariable Model Including Diastolic Blood Pressure and Its Interaction With Intraocular Pressure and All Other Covariate Effects on the Intercepts of Ganglion Cell Complex
eReferences
Data Sharing Statement