High Intraocular Pressure Is Independently Associated With New-Onset Systemic Hypertension Over a 10-Year Period

Abstract

Background: Systemic hypertension (HT) is associated with the development of increased intraocular pressure (IOP), a risk factor for glaucoma. However, it remains unclear whether high IOP is a risk factor for HT.

Methods and Results: We investigated 7,487 Japanese individuals (4,714 men, 2,773 women; mean [±SD] age 49±9 years) who underwent annual health checkups in 2006. Over the 10-year follow-up period, 1,232 (24.3%) men and 370 (11.5%) women developed new-onset HT, defined as initiation of antihypertensive drug treatment or blood pressure ≥140/90 mmHg. After dividing IOP into tertiles (T1–T3), Cox proportional hazards analysis (adjusted for age, sex, systolic blood pressure, obesity, current smoking, alcohol consumption, family history of HT, estimated glomerular filtration rate, and diabetes and dyslipidemia diagnoses at baseline) revealed a significantly higher risk of newly developed HT in T3 (IOP ≥14 mmHg; hazard ratio 1.14; 95% confidence interval 1.01–1.29; P=0.038) using T1 (IOP ≤11 mmHg) as the reference group. There was no significant interaction between sex and IOP tertile (P=0.153). A restricted cubic spline model showed a gradual but robust increase in the hazard ratio for new-onset HT with increasing IOP.

Conclusions: High IOP is an independent risk factor for the development of HT over a 10-year period.

Systemic hypertension (HT) is associated with several serious diseases, including chronic kidney disease, stroke, and cardiovascular diseases, which are the leading causes of death worldwide.¹ Despite global and regional efforts to prevent the development of HT, its prevalence remains high, and blood pressure management remains inadequate.² In addition to recent advances in the development of promising agents that can prevent cardiovascular and renal events,^3–5 proper identification of the risk factors for the development of HT and intervention at earlier stages of HT may be useful in reducing cardiovascular and renal diseases and extending healthy life expectancy.

Intraocular pressure (IOP) is one of the basic and physiological indices in the field of ophthalmology, and the measurement of IOP is essential for the diagnosis, pathological evaluation, and assessment of therapeutic efficacy of various ocular diseases, especially glaucoma.⁶ Glaucoma is characterized as a progressive optic neuropathy, and its progression can be controlled by reducing IOP levels.⁷ Although glaucoma is one of the leading causes of blindness worldwide,⁸ it has been noted that the rate of screening for glaucoma is not high and that there are probably many undiagnosed patients with glaucoma.⁹ Therefore, IOP measurement, which can be performed without the need for complicated techniques, should be part of annual heath checkups.

Systemic factors known to affect IOP are aging,¹⁰ sex,¹¹ racial differences,¹² exercise,¹³ and HT.^14,15 In fact, recent large cohort studies, including the Blue Mountains Eye Study in Australia¹⁶ and the Beaver Dam Eye Study in the US,¹⁷ have demonstrated an association between HT and increased IOP. However, because IOP is rarely considered as a risk factor for the development of HT in clinical settings in the internal medicine field, high IOP has not generally been included as a risk factor for the development of HT. Conversely, because fundoscopic examination can directly detect vascular damage in HT or atherosclerosis, more emphasis has been placed on ophthalmologists to perform fundoscopic examinations rather than measurement of IOP in evaluating the cardiovascular system.¹⁸

To the best our knowledge, most previous studies that focused on the association between HT and IOP or glaucoma were cross-sectional studies.^19,20 Therefore, whether high IOP is a potential risk factor for new-onset HT remains unclear. Thus, in the present study we investigated the association between IOP levels (even within the normal range) and new-onset HT over a 10-year follow-up period in a large number of subjects undergoing annual health checkups in Japan.

Methods

This study was a retrospective study using large-scale health checkup data in Japan. The study conformed to the principles outlined in the Declaration of Helsinki and was performed with the approval of the Institutional Ethics Committee of Sapporo Medical University (no. 30-2-32). Written informed consent was obtained from all subjects.

Study Subjects

All individuals who underwent annual health examinations at Keijinkai Maruyama Clinic (Sapporo, Japan) in 2006 were enrolled in this registry (n=28,990). A flow chart of the study participants is shown in Figure 1. Prespecified exclusion criteria were a diagnosis of HT at baseline and the absence of data on blood pressure and IOP. After the exclusion criteria had been applied, 7,487 individuals (4,714 men, 2,773 women) who had undergone a health checkup at least once in the period 2007–2016 were included in the present study. A self-administered questionnaire survey was used to obtain information on current smoking and alcohol consumption and the use of drugs for the treatment of HT, diabetes, and dyslipidemia.

Figure 1.

Flowchart showing selection of study participants. Among 28,990 subjects who underwent an annual health checkup in 2006, 7,487 (4,714 men, 2,773 women) were included in the present study.

Clinical Endpoint

The clinical endpoint was new-onset HT during the 10-year follow-up period, defined as systolic blood pressure (SBP) ≥140 mmHg, diastolic blood pressure (DBP) ≥90 mmHg, or self-reported use of antihypertensive medication in accordance with the guidelines of the Japanese Society of Hypertension.²¹

Measurements

Medical examinations, blood pressure measurement, and blood sampling were performed after an overnight fast. Blood pressure was measured twice consecutively on the arm using a sphygmomanometer (#601; Kenzmedico, Saitama, Japan), with the mean of the 2 blood pressure measurements used. IOP was measured using a non-contact tonometer (TX-F; Canon Medtech Supply Corp., Tokyo, Japan), which measures IOP 3 times and averages the values in both the left and right eyes. IOP levels of the left and right eyes were averaged for analyses. Body height and weight were measured in light clothing without shoes, and body mass index was calculated as body weight (kg) divided by height (m) squared. Estimated glomerular filtration rate (eGFR; mL/min/1.73 m²) was calculated using the following equation for Japanese people:²²

eGFR = 194 × serum creatinine^(−1.094) × age^(−0.287) × 0.739 (if female)

Obesity was defined as body mass index ≥25 kg/m² in accordance with the Japan Society for the Study of Obesity.²³ Diabetes was diagnosed in accordance with the guidelines of the American Diabetes Association as fasting plasma glucose ≥126 mg/dL, HbA1c ≥6.5%, or self-reported use of antidiabetic medication.²⁴ Dyslipidemia was diagnosed as low-density lipoprotein cholesterol ≥140 mg/dL, high-density lipoprotein cholesterol <40 mg/dL, triglycerides ≥150 mg/dL, or self-reported use of antidyslipidemic medication.

Statistical Analysis

Continuous variables are expressed as the mean±SD for those with a normal distribution and as the median with interquartile range (IQR) for those with a skewed distribution. The normality of distribution was tested for each parameter using the Shapiro-Wilk W test. Comparisons between 2 groups were performed using the Mann-Whitney U test.

Clinical parameters were divided into 3 subgroups according to IOP tertile (T1–T3) at baseline. Intergroup differences in percentages of demographic parameters were examined by the Chi-squared test. One-way analysis of variance was used to detect significant differences between data in multiple groups, and post hoc analysis was performed using Tukey’s multiple comparison procedure. The cumulative incidence of new-onset HT was analyzed by the log-rank test of Kaplan-Meier survival curves.

Cox proportional hazard model analysis was used to analyze the development of HT according to IOP tertile after adjustment for confounders, including age, sex, SBP, obesity, eGFR, current smoking, alcohol consumption, family history of HT, and diabetes and dyslipidemia diagnoses at baseline, as described previously.^25–28 The interaction between sex and IOP tertile for the development of HT was also investigated, and hazard ratios (HRs), 95% confidence intervals (CIs), and Akaike’s information criterion (AIC) were calculated. Parameters with a lower AIC score constitute a better-fit model.

The association between IOP at baseline as a continuous variable and the development of HT was also investigated by multivariable Cox proportional hazard models with a restricted cubic spline after adjustment for the confounders.

Two-tailed P values <0.05 was considered statistically significant. All data were analyzed using EZR²⁹ and R version 3.6.1 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria).

Results

Characteristics of the Study Subjects

Baseline characteristics of both included and excluded (based on prespecified exclusion criteria) subjects are presented in Supplementary Table 1. Excluded subjects were significantly younger than included subjects, and the percentage of men in the excluded groups was significantly lower than in the included group. SBP and DBP were significantly higher among excluded than included subjects. The characteristics of the included subjects, for the entire cohort and according to sex, are presented in Table 1. SBP, DBP, and IOP were significantly lower among men than women, and a significantly higher percentage of men had obesity, diabetes, and dyslipidemia than did women.

Table 1.

Baseline Characteristics of Study Subjects

	Total (n=7,487)	Men (n=4,714)	Women (n=2,773)	P value
Age (years)	48±9	49±9	47±9	<0.001
BMI (kg/m2)	22.8±3.2	23.7±3.0	21.3±3.1	<0.001
ObesityA	2,045 (27.3)	1,692 (35.9)	353 (12.7)
SBP (mmHg)	112±13	115±12	107±13	<0.001
DBP (mmHg)	72±9	74±8	68±9	<0.001
Current smoking habit	2,635 (35.2)	2,152 (45.7)	483 (17.4)	<0.001
Alcohol drinking habit	3,549 (47.4)	2,760 (58.5)	789 (28.5)	<0.001
Family history
HT	1,784 (23.8)	915 (19.4)	869 (31.3)	<0.001
Diabetes	193 (2.6)	165 (3.5)	28 (1.0)	<0.001
Comorbidity
Diabetes	342 (4.6)	293 (10.4)	49 (1.7)	<0.001
Dyslipidemia	1,729 (23.1)	1,048 (22.2)	681 (24.6)	0.02
IOP (mmHg)	13 [11–15]	13 [11–15]	13 [11–14]	<0.001
Biochemical data
Hemoglobin (g/dL)	14.3±1.5	15.1±1.0	12.9±1.2	<0.001
Platelets (×104/μL)	23.8±5.1	23.5±4.9	24.4±5.4	<0.001
Albumin (g/dL)	4.4±0.2	4.4±0.2	4.3±0.2	<0.001
Blood urea nitrogen (mg/dL)	14.2±3.3	14.7±3.3	13.3±3.2	<0.001
Creatinine (mg/dL)	0.73±0.20	0.80±0.20	0.59±0.08	<0.001
eGFR (mL/min/1.73 m2)	84.7±14.1	83.5±13.6	86.8±14.5	<0.001
Uric acid (mg/dL)	5.4±1.4	6.1±1.2	4.3±0.9	<0.001
AST (U/L)	23 [14–32]	25 [16–34]	20 [10–30]	<0.001
ALT (U/L)	25 [21–29]	30 [26–34]	18 [14–22]	<0.001
GGT (U/L)	46 [40–52]	59 [50–68]	24 [18–30]	<0.001
Fasting glucose (mg/dL)	92±19	94±20	86±12	<0.001
HbA1c (%)	5.3±0.6	5.4±0.7	5.2±0.5	<0.001
Total cholesterol (mg/dL)	205±34	206±34	204±34	<0.001
LDL-C (mg/dL)	124±32	125±31	119±31	<0.001
HDL-C (mg/dL)	61±16	56±14	70±15	<0.001
Triglycerides (mg/dL)	91 [62–135]	110 [78–159]	65 [49–91]	<0.001

Unless indicated otherwise, variables are expressed as n (%), mean±SD, or median [interquartile range]. ^AObesity was defined as a BMI ≥25 kg/m². ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; GGT, γ-glutamyl transpeptidase; HDL-C, high-density lipoprotein cholesterol; HT, hypertension; IOP, intraocular pressure; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure.

Table 2 presents the characteristics of subjects divided according to IOP tertile (T1–T3) at baseline. The T3 of IOP was associated with younger age, a higher percentage of men, higher SBP and DBP, higher eGFR, higher frequencies of obesity, alcohol consumption, diabetes, and dyslipidemia, and a lower frequency of current smoking.

Table 2.

Characteristics of Subjects Stratified by IOP Tertiles (n=7,487)

	IOP tertile			P value
	T1 (n=2,332)	T2 (n=2,141)	T3 (n=3,014)
IOP (mmHg)	≤11	12–13	≥14
Age (years)	49±9	48±9*	47±9*	<0.001
Male sex	1,408 (60.4)	1,326 (61.9)	1,980 (65.7)*,†	<0.001
BMI (kg/m2)	22.5±3.1	22.7±3.1	23.2±3.4*,†	<0.001
ObesityA	573 (24.1)	532 (24.8)	950 (31.5)*,†	<0.001
SBP (mmHg)	110±13	111±13*	114±13*,†	<0.001
DBP (mmHg)	70±9	71±9*	73±9*,†	<0.001
Current smoking habit	832 (35.7)	756 (35.3)	1,047 (34.7)	0.819
Alcohol drinking habit	995 (42.7)	970 (45.3)	1,584 (52.6)*,†	<0.001
Family history
HT	550 (23.6)	490 (22.9)	744 (24.7)	0.310
Comorbidity
Diabetes	63 (2.7)	80 (3.7)	199 (6.7)*,†	<0.001
Dyslipidemia	529 (22.7)	486 (22.7)	714 (23.7)	0.604
Biochemical data
Hemoglobin (g/dL)	14.1±1.5	14.2±1.6*	14.4±1.5*,†	<0.001
Platelets (×104/μL)	23.5±5.1	23.8±5.2	24.0±5.1*	0.007
Albumin (g/dL)	4.3±0.2	4.4±0.2	4.4±0.2*,†	<0.001
Blood urea nitrogen (mg/dL)	14.4±3.4	14.2±3.3	14.0±3.3*	0.002
Creatinine (mg/dL)	0.73±0.28	0.72±0.14	0.73±0.15	0.735
eGFR (mL/min/1.73 m2)	84.1±14.0	84.5±13.6	85.4±14.4*	0.003
Uric acid (mg/dL)	5.3±1.3	5.4±1.4	5.6±1.4*,†	<0.001
AST (U/L)	22 [17–24]	22 [17–25]	24 [18–26]*,†	<0.001
ALT (U/L)	23 [14–27]	25 [14–29]*	28 [15–32]*,†	<0.001
GGT (U/L)	40 [17–46]	46 [18–51]*	52 [20–61]*,†	<0.001
Fasting glucose (mg/dL)	89±14	91±17*	94±22*,†	<0.001
HbA1c (%)	5.3±0.5	5.3±0.6	5.4±0.8*,†	<0.001
Total cholesterol (mg/dL)	203±33	205±33	207±35*,†	<0.001
LDL-C (mg/dL)	122±31	123±31*	123±31*	0.044
HDL-C (mg/dL)	61±16	61±16	61±16	0.858
Triglycerides (mg/dL)	106 [59–127]	109 [62–131]	120 [66–146]*,†	<0.001

Unless indicated otherwise, variables are expressed as n (%), mean±SD, or median [interquartile range]. *P<0.05 compared with IOP Tertile 1 (T1); ^†P<0.05 compared with IOP T2. ^AObesity was defined as a BMI ≥25 kg/m². Abbreviations as in Table 1.

Cumulative Incidence of New-Onset HT During the Follow-up Period

The mean follow-up period was 6.0 years (range 1–10 years), and the follow-up summation was 45,001 person-years (27,258 and 17,743 person-years for men and women, respectively). Among the 7,467 subjects analyzed (4,714 men, 2,773 women), 1,232 (24.3%) men and 370 (11.5%) women developed new-onset HT over the 10-year follow-up period. The cumulative incidence of HT was 29.8% (95% CI 27.9–31.1) for the entire cohort (36.3% [95% CI 34.6–38.1] and 18.9% [95% CI 17.1–20.6] for men and women, respectively). Kaplan-Meier survival curves revealed a significant difference in the cumulative incidence of new-onset HT among the T1 (IOP ≤11 mmHg), T2 (IOP 12–13 mmHg) and T3 (IOP ≥14 mmHg) groups (log-rank test P<0.001; Figure 2).

Figure 2.

Kaplan-Meier survival curves showing the cumulative incidence of the development of hypertension (HT) according to intraocular pressure (IOP) tertile (T1–T3) at baseline.

Effect of Baseline IOP on New-Onset HT

In univariable analysis, using IOP T1 as the reference group, being in the T2 or T3 group was significantly associated with the development of HT (Model 1; Table 3). Multivariable Cox proportional hazard model analysis after adjustment for confounders, including age, sex, and SBP, showed a significantly higher risk of developing HT in the T3 group (HR 1.18; 95% CI 1.04–1.33) using T1 as the reference group (Model 2; Table 3). After incorporation of obesity, eGFR, current smoking, alcohol consumption, a family history of HT, and comorbidities of diabetes and dyslipidemia into Model 1 (Model 3), the risk of developing HT remained significantly higher in the T3 group (HR 1.14; 95% CI 1.01–1.29) relative to the T1 group, with Model 3 having the lowest AIC (24,570; Table 3). There were no significant interactions between sex and IOP tertiles for new-onset HT in Models 2 and 3 (Table 3).

Table 3.

Multivariable Cox Proportional Hazard Analyses for the Development of HT

	Model 1		Model 2		Model 3
	HR (95% CI)	P value	HR (95% CI)	P value	HR (95% CI)	P value
IOP
T1 (≤11 mmHg)	Ref.	–	Ref.	–	Ref.	–
T2 (12–13 mmHg)	1.17 (1.02–1.34)	0.023	1.05 (0.91–1.20)	0.504	1.06 (0.92–1.21)	0.434
T3 (≥14 mmHg)	1.49 (1.32–1.68)	<0.001	1.18 (1.04–1.33)	0.008	1.14 (1.01–1.29)	0.038
Age (per 1-year increase)	–	–	1.02 (1.02–1.03)	<0.001	1.02 (1.02–1.03)	<0.001
Male sex	–	–	1.09 (1.08–1.09)	<0.001	1.12 (0.98–1.28)	0.099
SBP (per 1-mmHg increase)	–	–	1.09 (1.07–1.08)	<0.001	1.09 (1.08–1.09)	<0.001
Obesity*	–	–	–	–	1.32 (1.18–1.46)	<0.001
eGFR (per 1-mL/min/1.73 m2 increase)	–	–	–	–	1.00 (0.99–1.002)	0.325
Current smoking habit	–	–	–	–	1.18 (1.06–1.32)	0.002
Alcohol drinking habit	–	–	–	–	1.27 (1.14–1.42)	<0.001
Family history of HT	–	–	–	–	1.37 (1.22–1.54)	<0.001
Diabetes	–	–	–	–	1.31 (1.07–1.21)	0.008
Dyslipidemia	–	–	–	–	1.14 (0.95–1.21)	0.242
Interaction: Sex-IOP tertiles		–	–	0.070	–	0.153
AIC	27,164		25,384		24,570

*Obesity was defined as a BMI ≥25 kg/m². AIC, Akaike’s information criterion; CI, confidence interval; HR, hazard ratio. Other abbreviations as in Table 1.

As an additional analysis, body mass index was used instead of obesity in Models 2 and 3 (Models 2′ and 3′, respectively; Supplementary Table 2). In Model 2′, the adjusted risk for the development of HT was significantly higher in the T3 group than in the T1 group used as the reference (HR 1.16; 95% CI 1.03–1.31; P=0.019; Supplementary Table 2). In Model 3′, the risk of developing HT tended to be higher in the T3 group using the T1 group as the reference (HR 1.12; 95% CI 0.99–1.27; P=0.072), although the difference was not statistically significant.

Multivariable Cox proportional hazard analysis with a restricted cubic spline model showed that the risk of developing HT increased with higher baseline IOP after adjusting for confounders including age, sex, SBP, obesity, eGFR, current smoking, alcohol consumption, family history of HT, and comorbidities of diabetes and dyslipidemia at baseline (Figure 3).

Figure 3.

(Top) Hazard ratios (HRs) for the development of systemic hypertension (HT) according to intraocular pressure (IOP) at baseline from a multivariable Cox proportional hazards model with a restricted cubic spline after adjusting for age, sex, systolic blood pressure, obesity, estimated glomerular filtration rate, current smoking, alcohol consumption, family history of HT, and comorbidities of diabetes and dyslipidemia at baseline. The solid line shows the HR; dashed lines show 95% confidence intervals. The reference value of IOP was 7 mmHg as the minimum value. (Bottom) Histograms showing the distribution of IOP in the study cohort.

Discussion

The present study showed that, among individuals undergoing annual health checkups, high IOP was independently associated with the risk of developing HT. Compared with the T1 IOP group (IOP ≤11 mmHg) used as the reference group, the T3 group (IOP ≥14 mmHg) had a significantly higher risk of developing HT after adjusting for age, sex, SBP, obesity, eGFR, current smoking, alcohol consumption, family history of HT, and comorbidities of diabetes and dyslipidemia at baseline. Furthermore, a restricted cubic spline model showed that the adjusted HR for the development of HT gradually and consistently increased with higher baseline IOP as a continuous variable. Considering that most of the subjects included in this study had IOP within the normal range (<20 mmHg), in accordance with the guidelines of the Japan Glaucoma Society,³⁰ our findings suggest that a high IOP even within the normal range is an independent risk factor for new-onset HT, regardless of the presence of glaucoma.

Although an association between increased IOP and HT has been shown in cross-sectional studies,^19,20 the strength of the present study is that new-onset HT could be chronologically predicted by baseline IOP in a large longitudinal study. To the best of our knowledge, there have been only 4 longitudinal studies investigating the association between IOP and blood pressure in apparently healthy adults.^31–34 A prospective study in 572 middle-aged US men showed that changes in IOP over 1–2 years were positively correlated with changes in SBP.³¹ Another study in 3,188 Malay and Indian adults showed that changes in IOP were positively correlated with changes in SBP and DBP over a 6-year follow-up period.³² A significant positive correlation between SBP and change in IOP was also shown in a longitudinal study assessing the effects of aging, blood pressure, and body mass index on IOP over an 8-year follow-up period in 68,998 Japanese individuals aged 20–79 years who underwent health examinations.³³ Furthermore, a 10-year longitudinal study in 15,747 Japanese individuals who underwent comprehensive medical examinations reported that the changes in IOP associated with 10-mmHg increases in SBP and DBP were 0.090 and 0.085 mmHg, respectively.³⁴ Although there were several differences in the number of people analyzed, race, observation period, purpose, and study endpoints, the results of these previous longitudinal analyses indeed support our findings that a high IOP was independently associated with newly developed HT over a 10-year follow-up period.

The present study also highlighted the clinical importance of routine IOP measurements to predict the development of HT, potentially leading to a reduction of risks for cardiovascular diseases. Even non-ophthalmologists can quickly and easily measure IOP using a non-contact tonometer such as the one used in the present study. A subtle elevation in IOP usually does not induce obvious symptoms.³⁵ It has also been reported that the majority of patients with glaucoma in Japan are categorized as having “normal tension glaucoma” (IOP ≤20 mmHg).⁹ Therefore, assessment of IOP in annual health checkups would be meaningful. Furthermore, a previous study showed that incorporation of ocular tests into routine checkups could increase the health checkup participation rate in Japan.³⁶ Taken together, the results suggest that measurements of both IOP and blood pressure should be re-evaluated to not only diagnose ocular diseases, but also to estimate the risk for cardiovascular diseases, including HT.

Because the present study was designed as a retrospective cohort study using subjects who underwent annual health examinations, the precise causal mechanisms remain unknown. However, there are several possible mechanisms for the link between high IOP and newly developed HT. The major factors for the regulation of IOP include aqueous humor dynamics with a trabecular meshwork and local circulation via aqueous humor production and outflow.^37,38 It has been reported that autonomic imbalances, including chronic sympathetic activation and parasympathetic suppression, are determinants that can control such aqueous humor dynamics.³⁹ Indeed, β-adrenergic receptor-blocking agents and muscarinic cholinergic activators have been clinically used in the treatment of glaucoma caused by a high IOP. Because it has been shown that autonomic abnormalities can contribute to the development of HT,⁴⁰ an abnormality of the autonomic nervous system can be postulated as a common pathophysiology for high IOP and the development of HT. Given that the production and drainage of aqueous humor in the anterior chamber of the eye takes place in the region of a very narrow closed lumen, increased IOP can precede the development of systemic HT.

Another candidate for the mechanism linking high IOP and the development of HT is the production of reactive oxygen species (ROS), which can be generated by a high IOP or glaucoma.⁴¹ It has recently been reported that extracellular calcium influx is induced by mechanical stress through the transient receptor potential vanilloid 4 channel⁴² and Piezo type mechanosensitive ion channel component 1⁴³ in most cells that constitute the eye. Although increased intracellular calcium in trabecular meshwork cells produces nitric oxide via calcium-induced endothelial nitric synthase activation,⁴⁴ a chronic increase in cytoplasmic calcium can activate NADPH oxidase to produce ROS and can affect the mitochondrial respiratory chain complex to generate mitochondria-derived ROS.⁴⁵ In cellular environments where ROS levels are high, nitric oxide can be catalyzed to reactive nitrogen species (RNS) such as peroxynitrite (ONOO⁻), thereby causing a redox imbalance through reduced antioxidant levels and cellular injury.⁴⁶ Increased release of ROS, RNS, and ROS/RNS-related molecules may induce further redox imbalance and cellular injury in local or systemic vascular endothelial cells, possibly leading to increased blood pressure. Because the present study is an observational study, these proposed molecular mechanisms by which increased IOP can cause HT need to be proven in future studies.

The present study has several limitations. First, there was a lack of complete ocular examinations, including slit-lamp examination, gonioscopy, and measurement of central corneal thickness, although the purpose of measuring IOP using a non-contact tonometer (which is convenient and has less variation between examiners) in the annual health checkups was to screen for glaucoma. In addition, measuring IOP once may vary in each individual, although it has been reported that the variation of IOP is little within the normal range (10–20 mmHg).^9,47 Second, because the individuals included in this study underwent annual health checkups at a single urban clinic, the possibility of sample selection bias cannot be ruled out. Third, risk factors for the development of HT at baseline were adjusted for in multivariable Cox proportional hazard models. However, those risk factors during the follow-up period were not used to adjust the models. Finally, we were not able to monitor blood pressure every week or month because the health checkups were conducted annually.

In conclusion, a high IOP, even within the normal range, was independently associated with new-onset HT over a 10-year follow-up period. Elucidation of the mechanisms underlying the development of HT in the presence of increased IOP may lead to the establishment of novel therapeutic strategies for HT.

Acknowledgments

The authors are grateful to Keita Numata and Takashi Hisasue for data management.

Sources of Funding

M.T., Y.A., and M.F. were supported by JSPS KAKENHI (Grant no. 21K09181, 22K08313, 23K07993).

Disclosures

The authors declare that they have no competing interests.

IRB Information

This study was approved by the Institutional Ethics Committee of Sapporo Medical University (No. 30-2-32).

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-24-0241

References

• 1.   Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The global burden of cardiovascular diseases and risk: A compass for future health. J Am Coll Cardiol 2022; 80: 2361–2371.
• 2.   NCD Risk Factor Collaboration. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: A pooled analysis of 1201 population-representative studies with 104 million participants. Lancet 2021; 398: 957–980.
• 3.   Sato T, Kouzu H, Yano T, Sakuma I, Furuhashi M, Tohse N. Potential favorable action of sodium-glucose cotransporter-2 inhibitors on sudden cardiac death: A brief overview. Front Cardiovasc Med 2023; 10: 1159953.
• 4.   Ussher JR, Drucker DJ. Glucagon-like peptide 1 receptor agonists: Cardiovascular benefits and mechanisms of action. Nat Rev Cardiol 2023; 20: 463–474.
• 5.   Lauder L, Mahfoud F, Azizi M, Bhatt DL, Ewen S, Kario K, et al. Hypertension management in patients with cardiovascular comorbidities. Eur Heart J 2023; 44: 2066–2077.
• 6.   Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology 2014; 121: 2081–2090.
• 7.   Nouri-Mahdavi K, Hoffman D, Coleman AL, Liu G, Li G, Gaasterland D, et al. Predictive factors for glaucomatous visual field progression in the Advanced Glaucoma Intervention Study. Ophthalmology 2004; 111: 1627–1635.
• 8.   Sun Y, Chen A, Zou M, Zhang Y, Jin L, Li Y, et al. Time trends, associations and prevalence of blindness and vision loss due to glaucoma: An analysis of observational data from the Global Burden of Disease Study 2017. BMJ Open 2022; 12: e053805.
• 9.   Iwase A, Suzuki Y, Araie M, Yamamoto T, Abe H, Shirato S, et al. The prevalence of primary open-angle glaucoma in Japanese: The Tajimi Study. Ophthalmology 2004; 111: 1641–1648.
• 10.   Asaoka R, Obana A, Murata H, Fujino Y, Omoto T, Aoki S, et al. The association between age and systemic variables and the longitudinal trend of intraocular pressure in a large-scale health examination cohort. Invest Ophthalmol Vis Sci 2022; 63: 22.
• 11.   Takahashi S, Hara K, Sano I, Onoda K, Nagai A, Yamaguchi S, et al. Systemic factors associated with intraocular pressure among subjects in a health examination program in Japan. PLoS One 2020; 15: e0234042.
• 12.   Kwon YH, Fingert JH, Kuehn MH, Alward WL. Primary open-angle glaucoma. N Engl J Med 2009; 360: 1113–1124.
• 13.   Risner D, Ehrlich R, Kheradiya NS, Siesky B, McCranor L, Harris A. Effects of exercise on intraocular pressure and ocular blood flow: A review. J Glaucoma 2009; 18: 429–436.
• 14.   Nislawati R, Taufik Fadillah Zainal A, Ismail A, Waspodo N, Kasim F, Gunawan A. Role of hypertension as a risk factor for open-angle glaucoma: A systematic review and meta-analysis. BMJ Open Ophthalmol 2021; 6: e000798.
• 15.   Yasukawa T, Hanyuda A, Yamagishi K, Yuki K, Uchino M, Ozawa Y, et al. Relationship between blood pressure and intraocular pressure in the JPHC-NEXT eye study. Sci Rep 2022; 12: 17493.
• 16.   Mitchell P, Lee AJ, Rochtchina E, Wang JJ. Open-angle glaucoma and systemic hypertension: The Blue Mountains Eye Study. J Glaucoma 2004; 13: 319–326.
• 17.   Klein BE, Klein R, Knudtson MD. Intraocular pressure and systemic blood pressure: Longitudinal perspective: The Beaver Dam Eye Study. Br J Ophthalmol 2005; 89: 284–287.
• 18.   Cheung CY, Biousse V, Keane PA, Schiffrin EL, Wong TY. Hypertensive eye disease. Nat Rev Dis Primers 2022; 8: 14.
• 19.   Leeman M, Kestelyn P. Glaucoma and blood pressure. Hypertension 2019; 73: 944–950.
• 20.   Wang X, Wang M, Liu H, Mercieca K, Prinz J, Feng Y, et al. The association between vascular abnormalities and glaucoma-what comes first? Int J Mol Sci 2023; 24: 13211.
• 21.   Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, et al. The Japanese Society of Hypertension guidelines for the management of hypertension (JSH 2019). Hypertens Res 2019; 42: 1235–1481.
• 22.   Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009; 53: 982–992.
• 23.   Examination Committee of Criteria for ‘Obesity Disease’ in Japan; Japan Society for the Study of Obesity. New criteria for ‘obesity disease’ in Japan. Circ J 2002; 66: 987–992.
• 24.   ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 2. Classification and diagnosis of diabetes: Standards of care in diabetes-2023. Diabetes Care 2023; 46(Suppl 1): S19–S40.
• 25.   Tanaka M, Sato T, Endo K, Inyaku M, Mori K, Hosaka I, et al. An increase in calculated small dense low-density lipoprotein cholesterol predicts new onset of hypertension in a Japanese cohort. Hypertens Res 2023; 46: 2635–2645.
• 26.   Mori K, Tanaka M, Hosaka I, Mikami T, Endo K, Hanawa N, et al. Metabolic dysfunction-associated fatty liver disease is associated with an increase in systolic blood pressure over time: Linear mixed-effects model analyses. Hypertens Res 2023; 46: 1110–1121.
• 27.   Mori K, Furuhashi M, Tanaka M, Higashiura Y, Koyama M, Hanawa N, et al. Serum uric acid level is associated with an increase in systolic blood pressure over time in female subjects: Linear mixed-effects model analyses. Hypertens Res 2022; 45: 344–353.
• 28.   Higashiura Y, Furuhashi M, Tanaka M, Takahashi S, Mori K, Miyamori D, et al. Elevated fatty liver index is independently associated with new onset of hypertension during a 10-year period in both male and female subjects. J Am Heart Assoc 2021; 10: e021430.
• 29.   Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 2013; 48: 452–458.
• 30.   Kiuchi Y, Inoue T, Shoji N, Nakamura M, Tanito M, Glaucoma Guideline Preparation Committee, Japan Glaucoma Society. The Japan Glaucoma Society guidelines for glaucoma 5th edition. Jpn J Ophthalmol 2023; 67: 189–254.
• 31.   McLeod SD, West SK, Quigley HA, Fozard JL. A longitudinal study of the relationship between intraocular and blood pressures. Invest Ophthalmol Vis Sci 1990; 31: 2361–2366.
• 32.   Chua J, Chee ML, Chin CWL, Tham YC, Tan N, Lim SH, et al. Inter-relationship between ageing, body mass index, diabetes, systemic blood pressure and intraocular pressure in Asians: 6-Year longitudinal study. Br J Ophthalmol 2019; 103: 196–202.
• 33.   Nomura H, Shimokata H, Ando F, Miyake Y, Kuzuya F. Age-related changes in intraocular pressure in a large Japanese population: A cross-sectional and longitudinal study. Ophthalmology 1999; 106: 2016–2022.
• 34.   Yokomichi H, Kashiwagi K, Kitamura K, Yoda Y, Tsuji M, Mochizuki M, et al. Evaluation of the associations between changes in intraocular pressure and metabolic syndrome parameters: A retrospective cohort study in Japan. BMJ Open 2016; 6: e010360.
• 35.   Chan KC, Poostchi A, Wong T, Insull EA, Sachdev N, Wells AP. Visual field changes after transient elevation of intraocular pressure in eyes with and without glaucoma. Ophthalmology 2008; 115: 667–672.
• 36.   Hiratsuka Y, Yokoyama T, Yamada M. Higher participation rate for specific health checkups concerning simultaneous ophthalmic checkups. J Epidemiol 2021; 31: 315–319.
• 37.   van Zyl T, Yan W, McAdams A, Peng YR, Shekhar K, Regev A, et al. Cell atlas of aqueous humor outflow pathways in eyes of humans and four model species provides insight into glaucoma pathogenesis. Proc Natl Acad Sci USA 2020; 117: 10339–10349.
• 38.   Cheng Y, Ren T, Wang N. Biomechanical homeostasis in ocular diseases: A mini-review. Front Public Health 2023; 11: 1106728.
• 39.   McDougal DH, Gamlin PD. Autonomic control of the eye. Compr Physiol 2015; 5: 439–473.
• 40.   Mancia G, Grassi G. The autonomic nervous system and hypertension. Circ Res 2014; 114: 1804–1814.
• 41.   Hurley DJ, Normile C, Irnaten M, O’Brien C. The intertwined roles of oxidative stress and endoplasmic reticulum stress in glaucoma. Antioxidants (Basel) 2022; 11: 886.
• 42.   Guarino BD, Paruchuri S, Thodeti CK. The role of TRPV4 channels in ocular function and pathologies. Exp Eye Res 2020; 201: 108257.
• 43.   Uchida T, Shimizu S, Yamagishi R, Tokuoka SM, Kita Y, Honjo M, et al. Mechanical stretch induces Ca²⁺ influx and extracellular release of PGE₂ through Piezo1 activation in trabecular meshwork cells. Sci Rep 2021; 11: 4044.
• 44.   Patel PD, Chen YL, Kasetti RB, Maddineni P, Mayhew W, Millar JC, et al. Impaired TRPV4-eNOS signaling in trabecular meshwork elevates intraocular pressure in glaucoma. Proc Natl Acad Sci USA 2021; 118: e2022461118.
• 45.   Gorlach A, Bertram K, Hudecova S, Krizanova O. Calcium and ROS: A mutual interplay. Redox Biol 2015; 6: 260–271.
• 46.   Salvagno M, Sterchele ED, Zaccarelli M, Mrakic-Sposta S, Welsby IJ, Balestra C, et al. Oxidative stress and cerebral vascular tone: The role of reactive oxygen and nitrogen species. Int J Mol Sci 2024; 25: 3007.
• 47.   Chen M, Zhang L, Xu J, Chen X, Gu Y, Ren Y, et al. Comparability of three intraocular pressure measurement: iCare pro rebound, non-contact and Goldmann applanation tonometry in different IOP group. BMC Ophthalmol 2019; 19: 225.