The arterial stiffness and pulse pressure are predictors of intraocular pressure in a middle-aged population

Abstract

The vascular theory posits that glaucoma is associated to ocular blood flow alterations. However, there is uncertainty regarding the association of arterial stiffness index (ASI) and pulse pressure (PP) to intraocular pressure (IOP). This study investigates the relationship between ASI and PP, and IOP, using the UK Biobank population. Among 89,930 participants of the UK Biobank population, ASI and PP were assessed, and their correlations with IOP were estimated using multiple linear and logistic regressions adjusted for age, sex, BMI, diabetes, dyslipidemia, mean blood pressure, heart rate, tobacco smoking status, previous CV diseases and antihypertensive therapy. Youden indices were determined for ASI at a value of 9.41 m/s and for PP at 49.18 mmHg to determine IOP > 21mmHg. After adjustment for all covariates, ASI > 9.41 m/s was significantly correlated with IOP levels, B = 0.054 [0.031; 0.076] and with IOP > 21 mmHg, OR = 1.057 [1.004–1.113]. PP > 49.18 mmHg was significantly associated with IOP levels, B = 0.378 [0.345; 0.401] and with IOP > 21 mmHg, OR = 1.558 [1.474–1.648]. Similar results were observed when considering ASI and PP at continuous levels. When considering participants with IOP < 21 mmHg (N = 83,079), ASI > 9.41 m/s and PP > 49.18 mmHg were significantly associated with IOP levels, B = 0.015 [0.008; 0.021], B = 0.286 [0.266; 0.306], respectively. We demonstrated that increased ASI and PP levels are associated with increased IOP. These findings provide new insight into the vascular theory between IOP and ASI.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-05639-0.

Introduction

Glaucoma is a complex disease characterized by progressive loss of retinal ganglion cells and damages to the optic nerve. It is a major public health treat, remaining the leading cause of irreversible blindness worldwide¹. Two main theories have been proposed as biological mechanisms of open-angle glaucoma (OAG): the mechanical and vascular theories. The mechanical theory suggests that high intraocular pressure (IOP) puts mechanical stress on retinal ganglion cells, leading to irreversible damage. While elevated intraocular pressure (IOP) and vascular dysregulation are well-established mechanisms, recent studies highlight the role of neuroinflammation and neurotoxicity in POAG progression. Neuroinflammation involves the activation of microglia and astrocytes, releasing pro-inflammatory cytokines such as TNF-α and IL-1β, creating a toxic microenvironment that accelerates RGC loss^2,3. Additionally, neurotoxicity, particularly glutamate excitotoxicity, leads to overstimulation of NMDA receptors, resulting in calcium overload, oxidative stress, and neuronal apoptosis^4,5.

Among European individuals, the most common form of this disease is primary open-angle glaucoma (POAG)⁶. High IOP is a major modifiable risk factor for POAG⁷ with an increased risk of 16% for every mmHg increase in IOP⁸. Lowering IOP remains the only therapy to slow the progression of vision loss associated with POAG⁹. However, this theory fails to explain why some patients with lowered IOP do not experience slowed disease progression¹⁰. In contrast, the vascular theory posits that glaucoma is associated with alterations in ocular blood flow¹¹ especially vascular dysfunction in normal-tension glaucoma^12,13. This theory is supported by the fact that cardiovascular disease is a risk factor for OAG^14,15. Vascular dysfunction, such as arteriosclerosis, can alter the microvascular environment of the optic disc and retinal, leading to vascular insufficiency¹⁶. Recent studies have shown that elevated pulse wave velocity (PWV) levels, a standard measurement of arterial stiffness (AS) are associated with structural damage progression in OAG^10,17. AS, mainly correlated with atherosclerosis, inflammatory processes and hypertension¹⁸ is also estimated by arterial stiffness index (ASI)¹⁹ and pulse pressure (PP)²⁰and could be considered as a main predictor of target organ damage¹⁹. To date, uncertainty remains regarding the association of ASI and PP with IOP. Therefore, this cross-sectional study investigated the relationship between ASI, PP and IOP (1) in the overall study population, (2) according to IOP lower or higher to 21 mmHg, and (3) according to glaucoma disease status, using the UK Biobank population.

Methods

UK biobank population

The UK Biobank cohort initially comprised 9.1 million eligible individuals, but 8.6 million either did not respond or did not provide consent. Consequently, at baseline, the UK Biobank included 502,478 Britons across 22 UK cities, drawn from the UK National Health Service Register between 2006 and 2010. These participants, aged between 38 and 73 years, represented 5.5% of the total UK Biobank cohort. Participants completed questionnaires; a computer-assisted interview; and underwent physical and functional measurements, as well as providing blood, urine, and saliva samples²¹. Data collected included personalized information from the participants, encompassing socio-economic, behavioral and lifestyle factors; a mental health batter: clinical diagnoses and therapies; genetics; imaging; and physiological biomarkers from blood and urine samples. The details of the cohort protocol are available in the literature²².

Ethical considerations

All participants provided electronic informed consent, and the UK Biobank received ethical approval from the North-West Multi-centre Research Ethics Committee (MREC) covering the whole of the UK. The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Northwest – Haydock Research Ethics Committee (protocol code: 21/NW/0157, date of approval: 21 June 2021). For details: https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics.

Study population

Among the 113,280 volunteers of the UK Biobank with IOP measurement, 1,346 were excluded due to missing data for ASI and PP. Subsequently, 23,350 were excluded due to a history of eye disorders/surgery/laser and missing data for other covariates, and extreme values of IOP, ASI and PP. Therefore, we analyzed data from 89,930 volunteers (Fig. 1).

Fig. 1.

Flowchart.

Intraocular pressure (IOP)

Ophthalmic assessment was not part of the original baseline assessment but was introduced as an enhancement in 2009 at six assessment centers across the UK (Liverpool and Sheffield in North England, Birmingham in the Midlands, Swansea in Wales, and Croydon and Hounslow in Greater London). Self-reported glaucoma status was ascertained by participants who selected “glaucoma” from a list of eye disorders in response to the question, “Has a doctor told you that you have any of the following problems with your eyes?”.

Participant IOP was measured once for each eye using the Ocular Response Analyzer (ORA; Reichert, Corp., Buffalo, NY). Participants who reported eye surgery within the previous four weeks or those with an eye infection were precluded from having IOP measured. The ORA is a non-contact tonometer that measures the force required to flatten the cornea using a jet of air. Unlike conventional non-contact tonometry, the ORA measures two pressures; firstly, when the cornea flattens on inward motion, and secondly when the cornea is flattened on outward motion. The average of these two pressures has been calibrated to derive a Goldmann-correlated IOP (IOPg), and the difference between these two pressures has been shown to be related to the biomechanical properties of the cornea²³. A linear combination of these two pressures has been developed to derive a corneal-compensated IOP (IOPcc)²⁴. We used IOPcc in our analyses as it is thought to provide the most accurate assessment of true IOP and is least affected by corneal properties²⁵.

We excluded participants with a history of laser or surgery for glaucoma, eye injury, corneal graft surgery, or refractive laser surgery, as these participants are likely to have IOP altered from physiological levels. To handle extreme values of IOP, we excluded IOP measurements in the top and bottom 0.5 percentiles.

A significant proportion of participants with the highest IOPs in the cohort will have been diagnosed and treated with IOP-lowering medication in the community before entering the current study. Data for pre-treatment IOP were not available, and excluding these participants would have truncated the study’s IOP distribution, thereby reducing statistical power for detecting associations with IOP. We therefore imputed pre-treatment IOP: in study participants reporting current IOP-lowering medication (N = 670), the measured IOP was divided by 0.7 based on the mean IOP reduction achieved by medication. This method has been used in previous studies for IOP^26–28. Participant IOP was calculated as the mean of right and left eye values for each participant with data available for both eyes. If data were only available for one eye, that value was considered the participant’s IOP.

Arterial stiffness index (ASI)

Pulse wave arterial stiffness index (ASI) was estimated using a non-invasive method during a volunteer’s visit to a UK Biobank Assessment Centre. Peripheral blood volume was measured by attaching a photoplethysmograph transducer (PulseTrace PCA 2^™, CareFusion, USA) to the volunteer’s resting finger, preferably the index finger of the non-dependent hand, although it could be placed on any finger²⁹. Volunteers were asked to breathe in and out slowly five times in a relaxed fashion, and readings were taken over 10–15 s. The carotid-to-femoral pulse transit time was estimated from the dicrotic waveform as the time difference between a forward compound, when the pressure is transmitted from the left ventricle to the finger and a reflected or backward compound as the wave travels from the heart to lower body via the aorta³⁰. ASI was calculated in meters per second (m/s) as: H/PTT, where H is the individual’s height, and PTT is the pulse transit time or the peak-to-peak time between the systolic and diastolic wave peaks in the dicrotic waveform³⁰. This methodology has been validated by independent studies comparing it with carotid femoral PWV, concluding that both measurement methods are highly correlated^30–33. ASI is a simple, operator-independent, non-expensive, and rapid method. Extreme outlier ASI values were excluded from the analyses, defined as top and bottom 0.5% extreme values^34,35.

Pulse pressure (PP)

Systolic and diastolic blood pressures (SBD, DBP) were measured twice at the assessment center using an automated BP device (Omron 705 IT electronic blood pressure monitor; OMRON Healthcare Europe B.V. Kruisweg 577 2132 NA Hoofddorp), or manually with a sphygmomanometer and an inflatable cuff in association with a stethoscope if the blood pressure device failed to measure BP or if the largest inflatable cuff of the device did not fit around the individual’s arm.

The participant was seated in a chair for all the measurements, which were carried out by nurses trained in performing BP measurements on the left upper arm³⁶. Multiple available measures for each participant were averaged. The Omron 705 IT BP monitor has met the Association for the Advancement of Medical Instrumentation SP10 standard and was validated by the British Hypertension Society protocol, with an overall “A” grade for both SBP and DBP³⁷. However, automated devices tend to measure higher BP compared to manual sphygmomanometers. Therefore, both SBP and DBP measured using the automated device were adjusted using algorithms by Stang et al.³⁸:

For SBP, the following algorithm was performed:

For DBP, the following algorithm was performed:

Pulse pressure (PP) was calculated as: SBP – DBP.

Extreme outlier PP values were excluded from the analyses (defined as top and bottom 0.5% extreme values)³⁹.

Laboratory and clinical parameters

Dyslipidemia was defined as having a fasting plasma total-cholesterol ≥ 6.61 mmol/L, LDL cholesterol ≥ 4.1 mmol/L, triglycerides level > 1.7 mmol/, or taking statins medication. Diabetes status was defined on either receiving anti-diabetic medication, being diabetes diagnosed by a doctor, or having a fasting glucose concentration ≥ 7 mmol/L. Medications were identified through the question: “Do you regularly take any of the following medications?”.

Biological parameters were detailed in the UK Biobank protocol⁴⁰.

Body mass index was calculated as weight (in kg) divided by heigh² (meter) and categorized as high (BMI > 30 kg/m²), moderate (BMI between 25 and 30 kg/m²) and low (less than 25 kg/m²).

Current tobacco smokers were defined as participants who responded “yes, on most or all days” or “yes, only occasionally” at the question “do you smoke tobacco now”.

CV diseases were defined by heart attack, angina, and stroke, as diagnosed by a doctor and reported in questionnaires.

Statistical analysis

Characteristics of the study population were described as the means with standard deviation (SD) for continuous variables, and as numbers and proportions for categorical variables. This study explored the correlations between ASI and PP with IOP levels.

Correlations between ASI, PP, and IOP levels were examined using multiple linear regression models, computing regression coefficients (B) and their 95% confidence interval (95% CI), followed by multiple logistic regression models with Odds ratio (OR) and 95% confidence interval for IOP > 21mmHg⁴¹. Based on the univariable logistic regression, the maximum Youden index, calculated as:

was chosen to determine the optimal decision thresholds (c) for discrimination for both ASI and PP according to IOP > 21mmHg. Multiple linear and logistic regression models were applied with these Youden indices. Firstly, models were adjusted for age (Model 1). Secondly, models were adjusted for Model 1 + sex, BMI, diabetes, dyslipidemia, mean blood pressure, heart rate, tobacco smoking status, previous CV diseases and antihypertensive therapy (Model 2). Antihypertensive medications were characterized by the question: “Do you regularly take any of the following medications?”. Cardiovascular diseases were defined by myocardial infarction, angina, and stroke as diagnosed by a clinician and reported in questionnaires. Statistics were performed using SAS software (version 9.4; SAS Institute, Carry, NC). A p value < 0.05 was considered statistically significant.

Results

The characteristics of the study population (N = 89,930) are described in Table 1. The study population comprised 1.32% of participants declaring glaucoma disease and 0.75% who were on IOP-lowering medication. In the overall study population, 7.62% had IOP levels higher to 21 mmHg.

Table 1.

Characteristics of the study population.

	Population N = 89,930
Age (years)	56.88	8.07
IOP (mmHg)	16.06	3.40
IOP > 21 mmHg	6851	7.62%
Glaucoma	1186	1.32%
IOP lowering medication	670	0.75%
ASI (m/s)	9.40	2.90
Pulse pressure (mmHg)	50.61	12.38
Women	48,136	53.18%
BMI (kg/m2)	27.45	4.78
SBP (mmHg)	132.37	16.90
DBP (mmHg)	81.76	8.24
Mean BP (mmHg)	98.63	10.32
ASPP-R	4.61	1.84
Heart rate (bpm)	68.51	10.91
Antihypertensive therapy	19,491	21.67%
Diabetes	6812	7.57%
Dyslipidemia	51,523	57.29%
Previous CV disease	4893	5.45%
Tobacco smoking
Current	8916	9.91%
Past	31,230	34.73%
Never	49,784	55.36%

ASPP-R: arterial stiffness – pulse pressure – ratio.

When considering the univariable relationship between ASI and IOP > 21mmHg, the Youden index was defined for ASI at the value of 9.41 m/s, p < 0.001 and for PP at the value of 49.18 mmHg, p < 0.001. Participants with IOP > 21mmHg showed higher ASI levels (9.74 (3.04) vs. 9.38 (2.89), p < 0.001) and higher PP levels (55.33 (12.78) vs. 50.23 (12.27), p < 0.001) than others. The same results were observed when considering glaucoma disease status according to IOP levels (Supplementary file 1).

Univariable linear relationship are described in Figs. 2 and 3, and according to the overall study population, the categorization of IOP lower or higher to 21mmHg, and according to the glaucoma disease status.

Fig. 2.

Univariable linear regressions between ASI and PP with IOP in overall study population and according to IOP levels status.

Fig. 3.

Univariable linear regressions between ASI and PP with IOP in population according to glaucoma status and IOP levels status.

In the overall study population, after adjustment for Model 1, ASI was significantly correlated with IOP (B = 0.025 [0.017;0.033]). After adjustment for all covariates (Model 2), ASI remained significantly correlated with IOP (B = 0.018 [0.011;0.026]) and significantly associated with IOP > 21mmHg (OR = 1.018 [1.009–1.027]). The same results were observed when considering ASI > 9.41 m/s with B = 0.051 [0.029;0.074] and OR = 1.057 [1.004–1.113] after adjustment for Model 2 (Table 2).

Table 2.

Linear and logistic regression models for the relationship between ASI and PP with IOP levels and IOP > 21mmHg in the overall study population.

	IOP levels N = 89,930		IOP > 21mmHg N = 89,930
	Model 1	Model 2	Model 1	Model 2
ASI levels	0.025 (0.017;0.033)	0.018 (0.011;0.026)	1.021 [1.011–1.031]	1.018 [1.009–1.027]
ASI > 9.41 m/s	0.051 (0.029; 0.074)	0.054 (0.031;0.076)	1.071 [1.018–1.126]	1.057 [1.004–1.113]
PP levels	0.041 (0.039;0.043)	0.039 (0.036;0.041)	1.023 [1.021–1.025]	1.021 [1.019–1.023]
PP > 49.18mmHg	0.421 (0.398;0.444)	0.378 (0.354;0.401)	1.655 [1.567–1.747]	1.558 [1.474–1.648]

In the overall study population (N = 89,930), after adjustment for Model 1, PP was significantly correlated with IOP (B = 0.041 [0.039;0.043]). After adjustment for all covariates (Model 2), PP remained significantly correlated with IOP (B = 0.039 [0.036;0.041]) and significantly associated with IOP > 21mmHg (OR = 1.021 [1.019–1.023]). The same results were observed when considering PP > 49.18 mmHg with B = 0.378 [0.354;0.401] and OR = 1.558 [1.474–1.648] after adjustment for Model 2 (Table 2).

When considering participants with IOP < 21mmHg (N = 83,079), ASI was significantly correlated with IOP (for Model 2, B = 0.015 [0.008; 0.021]). The same results were observed for ASI > 9.41 m/s with IOP (for Model 2, B = 0.041 [0.022; 0.060]), with PP with IOP (for Model 2, B = 0.029 [0.027;0.030], and with PP > 49.18 mmHg with IOP (for Model 2, B = 0.275 [0.255; 0.294]) (Table 3). No significant adjusted linear associations were observed between ASI and PP with IOP among participants with IOP > 21mmHg (N = 6,851) (Table 3). However, nonlinear relationships were observed between ASI with IOP (IOP = log(2.956 + 3.156*ASI), p = 0.015) and between PP and IOP (IOP = 21.851 + 0.368*log(PP), p < 0.001) among participants with IOP > 21mmHg.

Table 3.

Linear regression models for the relationship between ASI and PP with IOP levels and IOP > 21mmHg in the population with IOP < 21mmHg and with IOP > 21mmHg.

	IOP < 21mmHg N = 83,079		IOP > 21mmHg N = 6,851
	Model 1	Model 2	Model 1	Model 2
ASI levels	0.017 (0.010;0.023)	0.015 (0.008;0.021)	0.006 (−0.012;0.023)	0.003 (−0.015;0.022)
ASI > 9.41 m/s	0.035 (0.016;0.054)	0.041 (0.022;0.060)	0.019 (−0.034;0.737)	0.009 (−0.046;0.063)
PP levels	0.030 (0.029;0.032)	0.029 (0.027;0.030)	0.003 (−0.002;0.007)	0.001 (−0.004;0.005)
PP > 49.18mmHg	0.309 (0.290;0.329)	0.286 (0.266;0.306)	0.007 (−0.051;0.066)	0.023 (−0.036;0.083)

When considering glaucoma disease status, only significant correlations were observed in non-glaucoma participants with IOP < 21mmHg (N = 82,516), between ASI and IOP levels (for Model 2, B = 0.015 [0.008; 0.021]), between ASI > 9.41 m/s and IOP levels (for Model 2, B = 0.042 [0.023; 0.061], between PP and IOP levels (for Model 2, B = 0.029 [0.027; 0.031]) and between PP > 49.18 mmHg and IOP levels (for Model 2, B = 0.288 [0.268; 0.308]) (Table 4). No correlation was observed between IOP levels and ASI or PP in participants with glaucoma with IOP > 21mmHg (N = 623), participants with glaucoma with IOP < 21mmHg (N = 563), or in participants with non-glaucoma disease and IOP > 21mmHg (N = 6,228) (Table 4). However, when considering the univariable correlation between PP with IOP, PP showed a significant nonlinear correlation with IOP in non-glaucoma participants with IOP > 21mmHg (log(IOP) = 3.104 + 0.004*Sprt(PP), p < 0.001) and a linear relationship in glaucoma participants with IOP < 21mmHg (p = 0.022) (Fig. 3). When considering the univariable correlation between ASI with IOP, ASI showed a significant nonlinear relationship with IOP in non-glaucoma participants with IOP > 21mmHg (IOP = log(1.74 + 2.53*ASI), p = 0.023) and a trend towards a linear relationship between ASI and IOP in glaucoma participants with IOP < 21mmHg (p = 0.094) (Fig. 3).

Table 4.

Linear regression models for the relationship between ASI and PP with IOP levels and IOP > 21mmHg in the population of glaucoma or not, according to the IOP level.

	Glaucoma with IOP < 21mmHg N = 563		Glaucoma with IOP > 21mmHg N = 623
	Model 1	Model 2	Model 1	Model 2
ASI levels	0.031 (−0.045;0.107)	0.016 (−0.062;0.094)	0.033 (−0.043;0.109)	0.019 (−0.059;0.097)
ASI > 9.41 m/s	0.126(−0.102;0.354)	0.087 (−0.146;0.321)	0.138 (−0.086;0.362)	0.124 (−0.107;0.355)
PP levels	0.009 (−0.010;0.028)	0.009 (−0.010;0.029)	0.008 (−0.011;0.026)	0.005 (−0.013;0.025)
PP > 49.18mmHg	0.185 (−0.051;0.422)	0.169 (−0.081;0.419)	0.031 (−0.212;0.274)	0.062 (−0.1844;0.309)

	No glaucoma with IOP < 21mmHg N = 82,516		No glaucoma with IOP > 21mmHg N = 6,228
	Model 1	Model 2	Model 1	Model 2
ASI levels	0.017 (0.010;0.023)	0.015 (0.008;0.021)	0.002 (−0.014;0.20)	0.006 (−0.013;0.022)
ASI > 9.41 m/s	0.034 (0.015;0.053)	0.042 (0.023;0.061)	0.011 (−0.040;0.062)	0.040 (−0.013;0.093)
PP levels	0.031 (0.028;0.032)	0.029 (0.027;0.031)	0.005 (−0.001;0.009)	0.003 (−0.001;0.008)
PP > 49.18mmHg	0.311 (0.291;0.330)	0.288 (0.268;0.308)	0.041 (−0.015;0.097)	0.018 (−0.039;0.075)

Collinearities between continuous variables are described in supplementary file 2.

Supplementary file 3 showed the associations of the confounding factors with IOP levels.

Sensitivity analysis

The correlation analysis reveals a weak positive association between ASI and PP (r = 0.1361), suggesting that these two parameters capture distinct but related aspects of vascular function. Thus, in the overall study population (N = 89,930), after adjustment for all covariates (Model 2), when considering both ASI and PP in the same model, ASI and PP remained significantly correlated with IOP (for ASI, B = 0.016 [0.008;0.023] and for PP, B = 0.013 [0.011;0.016]) and significantly associated with IOP > 21mmHg (for ASI, OR = 1.012 [1.003–1.018] and for PP, OR = 1.011 [1.007–1.014]).

Given this relationship, we further developed the “Arterial Stiffness and Pulse Pressure Ratio” (ASPP-R):

This index aims to provide a more integrated measure of arterial stiffness and vascular load, reflecting both the mechanical properties of the arterial wall and the hemodynamic burden, while accounting for overall blood pressure levels. This approach could enhance the predictive value for cardiovascular outcomes, particularly when both parameters are considered in a combined model.

Thus, in the overall population, after adjustment for all covariates, the ASPP-R remained significantly correlated with IOP (B = 0.077 [0.064;0.091]) and significantly associated with IOP > 21mmHg (OR = 1.051 [1.036–1.065]).

Discussion

This cross-sectional study demonstrated that among a general middle-aged population, ASI and PP were significantly correlated with IOP levels. These findings align with previous studies indicating a positive relationship between PP and IOP^25,42,43 and between AS, particularly determined by PWV, and IOP^10,44. In line with the vascular theory, we found that the relationship between ASI and PP primarily occurred among normal IOP participants^12,13. Furthermore, this study identified that the cutoff of ASI values was 9.41 m/s and that the cutoff of PP values was 49.18 mmHg to determine IOP > 21mmHg. This finding is consistent with some European consortiums which have reported normal references for PWV, as values under the cut-off of 10 m/s⁴⁵ and with PP indicating normal values under 50 mmHg⁴⁶.

Arterial stiffness and intraocular pressure

Arterial stiffness is a promising therapeutic target in response to vascular aging⁴⁷. It occurs when its cells and extracellular matrices undergo several biological modifications, potentially leading to an overall compromise of its flexibility and rigidity. As IOP increases, vessels may be unable to maintain perfusion, leading to optic nerve damages. High pulse pressure, in association with arterial stiffness, could increase the risk of OAG¹⁶. This theory suggests that vascular system could be a risk factor of elevated IOP in glaucoma⁴⁸. Thus, previous investigations have shown that stiffness observed in tissues related to glaucoma was associated with increased IOP⁴⁹. Furthermore, as observed in this study, some patients might not develop glaucoma despite the presence of elevated IOP⁵⁰. Numerous clinical studies directly measuring systemic and ocular fluid dynamics of blood flow have confirmed the correlation of vascular dysregulation with many risk factors for glaucoma⁵¹.

Pathophysiological relationship between arterial stiffness and intraocular pressure

An increase in IOP can be caused by several resistances to the flow of aqueous humor through the trabecular outflow pathway⁵². Dysregulation of this tissue through various mechanisms may disrupt the flow of the aqueous humor, potentially initiating an increase in IOP⁵³. A previous study showed that the glaucomatous trabecular meshwork was generally stiffer than that of normal subjects⁵⁴. Systemic vascular involvement can manifest as trabecular vascular damage leading to an elevation of intraocular pressure, thereby causing reduced permeability of the trabeculum within the eye⁵⁵.

Moreover, some studies have suggested a possible interaction between arterial stiffness and the peripapillary sclera surrounding the optic nerve head (ONH)⁵³particularly in glaucoma⁴⁹. The sclera, being the primary load-bearing structure of the eye, responds biomechanically to increased IOP, thereby modulating the extent of force applied to the ONH⁵⁶. Fundamental investigations have indicated that the structure of the sclera may change in response to elevated blood pressure and increased arterial stiffness⁵⁶.

We hypothesize that arterial stiffness, indicated by high values of ASI or PP, may lead to the disruption of ocular autoregulation with the loss of axonal fibers due to chronic circulatory failure of the optic nerve head and transient vascular spasms⁵⁷. Arterial stiffness is a major risk factor for cardiovascular diseases⁵⁸. Pulse wave velocity (PWV) has traditionally been used as the gold standard for determining arterial stiffness, but it depends critically on the accurate placement of transducers over the arteries and is both time-consuming and complex⁵⁹. For quantification of arterial stiffness, we assessed ASI and PP as valuable alternatives to PWV, applicable to large-scale epidemiological studies¹⁹. The reason why ASI may be significant is a question worthy of further research. Vascular endothelial cells produce and release numerous vasoactive components, which can play a significant role in maintaining vascular functions, including vasodilation, inhibition of platelet adhesion, and the proliferation of vascular smooth muscle cells⁶⁰. Previous investigations have shown that many risk factors, including age, glycemia, high blood pressure, blood lipid level imbalance, may lead to vascular endothelial damage, which in turn leads to an imbalance of vasoactive factors produced by endothelial cells. These factors may lead in changes in the structure of vascular wall, resulting in decreased vascular compliance⁶¹. The reduction in vascular compliance leads to a dysregulation of endothelial dysfunction, which occurs earlier than atherosclerotic plaque formation⁶². A high ASI level indicates poorer arterial elasticity⁶³and the loss of arterial elasticity could be a risk factor for glaucoma, contributing of the elevation of IOP^10,16.

Subgroup inconsistencies

The observed variability in the effect sizes and significance levels of ASI and PP across different subgroups in this study requires further consideration. In particular, the lack of significant associations in participants with glaucoma and IOP > 21 mmHg may reflect distinct pathophysiological mechanisms within this subgroup. Elevated IOP, a hallmark of glaucoma, can significantly alter ocular blood flow and retinal perfusion, potentially masking the effects of systemic arterial stiffness^64,65. Additionally, the influence of IOP on optic nerve head biomechanics and axonal transport may further complicate the relationship between systemic vascular measures and intraocular pressure regulation⁶⁶. The subgroup-specific variability highlights the need for more precise phenotyping in future studies, including the consideration of factors such as baseline IOP, duration of glaucoma, and the presence of vascular comorbidities. It also suggests that systemic vascular markers like ASI and PP may have limited predictive value in certain high-risk populations unless combined with more targeted ocular measurements.

Furthermore, these findings underscore the importance of integrating multimodal assessments, including retinal imaging and ocular perfusion metrics, to capture the full spectrum of vascular dysfunction in glaucoma.

Arterial stiffness and pulse pressure ratio (ASPP-R)

The introduction of the ASPP-R as a novel composite measure offers a promising approach for evaluating vascular health. Traditional measures like ASI and PP, while informative, capture only partial aspects of arterial function. ASI primarily reflects the mechanical stiffness of the arterial wall, which is influenced by structural changes such as collagen deposition and elastin fragmentation^19,67 while PP represents the dynamic hemodynamic load exerted on the arterial system⁶⁸. By combining these two parameters and normalizing by MAP, the ASPP-R provides a more comprehensive index that incorporates both the mechanical properties and the pressure dynamics of the arterial system, potentially enhancing its predictive power for adverse cardiovascular outcomes.

Several studies have demonstrated that increased ASI is independently associated with higher cardiovascular risk, including hypertension, stroke, and heart failure⁶⁹. Similarly, elevated PP has been linked to greater arterial stiffness and higher all-cause mortality⁷⁰. However, these markers often capture overlapping but distinct physiological processes, which may explain the relatively modest correlation observed in this study (r = 0.1361). The ASPP-R, by integrating these components, may offer a novel assessment of vascular risk.

Moreover, the normalization by MAP accounts for the overall blood pressure context, reducing the influence of extreme pressure values and potentially improving the specificity of the index for detecting early vascular dysfunction⁵⁸. This approach aligns with recent evidence suggesting that composite indices combining multiple hemodynamic parameters may provide superior risk stratification in diverse populations⁷¹.

Future studies should focus on validating the ASPP-R in larger, prospective cohorts and across different clinical settings to establish its predictive value for long-term cardiovascular outcomes. Additionally, integrating this index with emerging biomarkers of vascular health, such as central aortic pressure and wave reflection indices, could further refine its clinical utility⁷².

Limitations

The principal strength of this investigation is its very large sample size. Moreover, the use of the Pulse Trace device to measure AS accounts for greater variability in ASI values compared to other available devices⁷³. The UK Biobank study had a low responses rate of 5.5%, and potential volunteer bias may be present. However, given the large sample size and high internal validity of the UK Biobank protocol, these limitations are unlikely to significant impact the observed associations^74,75. Additionally, the study cohort consisted of middle-aged English participants, limiting the generalizability of the results to other age groups and ethnicities. The UK Biobank relied on standardized protocols for collecting anthropometric data, ensuring the reproductibility of data collection regardless of when, where, and by whom the measurements were taken. However, our study has several limitations: the ASI values obtained using the UK Biobank methodology were not the gold standard, such as PWV, and do not accurately measure central arterial stiffness. This could bias the observed results. Nonetheless, this ASI measurement has been validated by three independent studies in comparison with PWV, with these studies concluding that both measures are largely correlated^31–33. The self-reported nature of glaucoma diagnoses may affect the observed correlations due to potential misclassification errors. Moreover, no specific information was added concerning the severity of glaucoma and the evolution of the disease. Another significant limitation of our study was that only one IOP measurement was performed for each volunteer, making the data more susceptible to measurement errors than if multiple measurements had been taken. However, the standard deviation for IOP was 3.4 mmHg, which is close to that of a previous study that used an average of three measurements, with a standard deviation of 3.7 mmHg⁷⁶. While the photoplethysmography (PPG) transducer used in this study provides a practical, non-invasive approach for measuring carotid-to-femoral pulse transit time (PTT) and estimating ASI, it also has several inherent limitations. Notably, the use of a single-site PPG sensor may introduce measurement inaccuracies due to factors such as variable arterial wall thickness, sensor positioning, and signal quality⁷⁷. Additionally, single-site PPG assessments may be less accurate than multi-site measurements, which capture the full propagation of the pulse wave, potentially leading to variability in ASI estimation⁷⁸.

Conclusion

In conclusion, we observed in this large middle-aged population that arterial stiffness, as measured by ASI and PP, was correlated with IOP, particularly among participants with normal IOP levels. Moreover, we found that the thresholds for ASI and PP indicating high IOP values (exceeding 21 mm Hg) were close to the range of values observed for cardiovascular diseases, such as coronary artery disease. These findings offer insights into the vascular theory through the demonstrated link between IOP and arterial stiffness.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization: AV, formal analysis: AV, writing – original draft preparation: A.V, editing and writing: AV, AL, MA, CB, JNV. Validation: AV, AL, MA, CB, JNV. Only AV had access to the dataset. The authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data availability

UK Biobank data are available through the UK Biobank Access Management System (UK Biobank Access Management System: http://www.ukbiobank.ac.uk/register-apply/).

Declarations

Competing interests

The authors declare no competing interests.

Ethics of approval statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Northwest – Haydock Research Ethics Committee (protocol code: 21/NW/0157, date of approval: 21 June 2021).

Patient consent statement

Written informed consent has been obtained from the patients.