Abstract
Background
Glaucoma, the second leading cause of blindness worldwide, is associated with elevated intraocular pressure (IOP). Metabolic syndrome, a cluster of conditions that includes obesity, hypertension, hyperglycemia, and dyslipidemia, has been linked to increased IOP. This study investigated the relationship between IOP and metabolic syndrome risk factors in an adult Korean population.
Methods
Data from 1,896 adults (aged 19–59 years) from the 2021 Korea National Health and Nutrition Examination Survey were analyzed. The exclusion criteria were age < 19 years; use of anti-glaucoma medications or anti-inflammatory eye drops; history of glaucoma, retinal, or refractive surgery; and a diagnosis of glaucoma. Metabolic syndrome risk factors, including blood pressure, fasting glucose, fasting insulin, homeostasis model assessment of insulin resistance, body mass index, abdominal obesity, triglycerides, and high-density lipoprotein cholesterol were examined for their association with ocular hypertension (OHT) using multivariate logistic regression.
Results
Participants with metabolic syndrome (n = 383, 20.3%) demonstrated a higher mean IOP (16.05 ± 0.15 mmHg) compared with the normal group (15.33 ± 0.09 mmHg; P < 0.001). OHT was present in 33 patients (1.2%), and its prevalence did not differ significantly between the normal and metabolic syndrome groups. In multiple linear regression, risk factors, including hyperglycemia (P = 0.037) and hypertriglyceridemia (P = 0.027), were significantly associated with IOP. Abdominal obesity showed a strong association with OHT in multivariate analysis (odds ratio, 2.90; 95% confidence interval, 1.35–6.23; P = 0.007).
Conclusion
Risk factors for metabolic syndrome, particularly abdominal obesity, were strongly associated with OHT. These findings emphasize the importance of regular IOP screening to prevent glaucoma progression in individuals with metabolic syndrome.
Keywords: Intraocular Pressure, Metabolic Syndrome, Obesity, Ocular Hypertension
Graphical Abstract
INTRODUCTION
Glaucoma is a leading cause of irreversible blindness, with a global prevalence of 3.54% among adults aged 40–80 years.1,2 By 2040, the number of affected individuals is projected to rise from 76 million in 2020 to 111 million, posing significant challenges for healthcare systems worldwide.2 In Korea, the prevalence of glaucoma is approximately 3.5%, with an upward trend observed in recent decades.3,4
Intraocular pressure (IOP) is a critical modifiable risk factor in glaucoma pathogenesis. Ocular hypertension (OHT), defined as an IOP > 22 mmHg without optic nerve damage, is a well-established precursor to glaucomatous changes.5 Elevated IOP imposes mechanical stress on the lamina cribrosa, leading to optic nerve deformation, retinal ganglion cell apoptosis, and eventual visual field deficits.6,7,8 Given its pivotal role in glaucoma development, OHT warrants regular ophthalmic management to mitigate disease progression.
Metabolic syndrome, a constellation of abdominal obesity, hypertension, dyslipidemia, hyperglycemia, and insulin resistance, is becoming increasingly prevalent in Korea due to shifts in dietary habits and lifestyle.9,10,11,12,13 Recent evidence highlights a significant relationship between metabolic syndrome and elevated IOP. For instance, higher systolic blood pressure (SBP) and hyperglycemia increase IOP through mechanisms such as enhanced aqueous humor production and osmotic fluid shifts.9,10,11 Obesity contributes to elevated episcleral venous pressure due to intraorbital fat deposition, whereas dyslipidemia increases blood viscosity and impedes aqueous humor outflow.12 These pathophysiological mechanisms collectively underscore the potential link between metabolic syndrome and OHT.
The diagnostic criteria for metabolic syndrome have evolved since their initial definition by the World Health Organization in 1998.14,15 Subsequent refinements by organizations such as the National Cholesterol Education Program (NCEP), International Diabetes Federation, and American Heart Association emphasize key components, including abdominal obesity, hypertension, hyperglycemia, and dyslipidemia.16,17 These risk factors exacerbate vascular dysfunction, systemic inflammation, and ectopic fat deposition, which may further disrupt aqueous humor dynamics and elevate episcleral venous pressure.
In Korea, the prevalence of metabolic syndrome increased from 27.1% in 2001 to 33.2% in 2020, reflecting the impact of Westernized dietary patterns.18 This increasing prevalence is anticipated to drive higher rates of OHT and glaucoma, with significant societal and economic consequences. While previous studies have investigated the association between metabolic syndrome and OHT, few have systematically analyzed the individual risk factors within the Korean population.
This study investigated the relationship between IOP and specific components of metabolic syndrome in Korean adults. By identifying key metabolic risk factors associated with elevated IOP, we aimed to inform targeted prevention and management strategies for glaucoma, particularly among high-risk groups.
METHODS
Participants
This study analyzed data from the 2021 Korea National Health and Nutrition Examination Survey (KNHANES), conducted by the Korea Centers for Disease Control and Prevention, to evaluate the association between IOP and metabolic syndrome. The 2021 KNHANES performed ophthalmic examinations on participants aged < 60 years. Among the 7,090 participants initially recruited, the present study excluded those who did not undergo an ophthalmic examination. Additional exclusion criteria were age < 19 years; use of anti-glaucoma medications or anti-inflammatory eye drops; history of glaucoma, retinal, or refractive surgery; and glaucoma diagnosis. Participants with at least one missing value for a metabolic syndrome component were also excluded. Finally, the present study included 1,896 participants (816 male and 1,080 female) (Fig. 1). The participants were stratified into groups based on the presence or absence of metabolic syndrome to compare their IOP and the prevalence of OHT. Additionally, individuals were categorized into normal and OHT groups to investigate the relationship between OHT and risk factors for metabolic syndrome.
Fig. 1. Flow chart of participant selection. After applying the exclusion criteria, a total of 1,896 participants were selected from the 7,090 participants in the 2021 Korea National Health and Nutrition Examination Survey.
Study objectives and significance
The primary goal of this study was to elucidate the interplay between metabolic syndrome and elevated IOP, with a specific focus on individual metabolic risk factors in the Korean adult population. By identifying key contributors to OHT, this study aimed to provide insights into tailored preventive strategies and early interventions for glaucoma among high-risk individuals. Considering the increasing prevalence of metabolic syndrome in Korea, understanding these associations is crucial for mitigating the potential societal and healthcare burdens of OHT and glaucoma.
By comprehensively analyzing the components of metabolic syndrome and their associations with OHT, this study sought to bridge the gap in the existing literature, particularly within the Korean population. These findings highlight the importance of routine screening and metabolic risk factor management to reduce the burden of glaucoma and improve public health outcomes.
Variables and diagnostic criteria
The dependent variable, IOP, was measured in the right eye, with OHT defined as IOP > 22 mmHg.19 Independent variables included age, sex, SBP, diastolic blood pressure (DBP), fasting glucose, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), body mass index (BMI), waist circumference (WC), triglycerides, and high-density lipoprotein cholesterol (HDL-C).
Metabolic syndrome was defined according to the NCEP Adult Treatment Panel III and Korean Society of Obesity criteria as the presence of at least three of the following 5 factors16,20:
1) WC ≥ 90 cm in males and ≥ 85 cm in females
2) HDL-C level < 40 mg/dL in males and < 50 mg/dL in females
3) Triglyceride level ≥ 150 mg/dL
4) SBP ≥ 130 mmHg or DBP ≥ 85 mmHg
5) Fasting glucose ≥ 100 mg/dL
Measurement
All participants completed ophthalmologic surveys, which included questions about eyedrop use and history of ophthalmologic surgery. No responses were missing. IOP was measured using the iCare Rebound Tonometry PRO (Icare Finland, Oy, Finland). Six readings were taken at the center of the cornea and the mean of the middle four values (excluding the highest and lowest) was used. To exclude participants with glaucoma, diagnoses were made based on the International Society for Geographic and Epidemiological Ophthalmology criteria.21 Diagnosis of glaucoma requires structural optic nerve damage with or without corresponding visual field defects. The optic disc was evaluated using fundus photography (VISUCAM 224; Carl Zeiss Meditec AG, Jena, Germany) and optical coherence tomography (CIRRUS HD 500; Carl Zeiss Meditec, Inc., Dublin, CA, USA). Visual field tests were performed using frequency-doubling technology (Humphrey Matrix 800; Carl Zeiss Meditec, Inc.). Board-certified ophthalmologists affiliated with the Korean Ophthalmological Society reviewed all imaging data to diagnose glaucoma.
Blood pressure in the upper arm was measured using a non-mercury oscillometric sphygmomanometer (Microlife WatchBP Office, Widnau, Switzerland). Measurements were taken at 5-minute intervals with the participant in a seated position. The average of the second and third readings was used.
Weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively, without shoes or headwear, and BMI was calculated as weight (kg) divided by height squared (m2). WC was measured to the nearest 0.1 cm at the midpoint between the bottom of the last rib and the top of the iliac crest.
Blood samples were collected after at least 8 hours of fasting to measure glucose, triglyceride, and high-density lipoprotein cholesterol levels using a Labospect008AS analyzer (Hitachi, Tokyo, Japan). Fasting insulin was measured using a Cobas 8000 e801 analyzer (Roche, Basel, Switzerland), and hyperinsulinemia was defined as values ≥ 13 μIU/mL.22 Insulin resistance was assessed using the HOMA-IR, calculated as:
| HOMA-IR = Fasting Insulin (μIU/mL) × Fasting Glucose (mg/dL)/405 |
Insulin resistance was defined as HOMA-IR ≥ 2.34.22
Statistical analysis
The KNHANES provides sample weights to estimate the prevalence in the Korean population. Thus, this study applied weights that accounted for complex sampling in the statistical analysis. Continuous variables were assessed using t-tests. Categorical variables were analyzed using the χ2 test. The associations between independent variables and IOP were evaluated using multiple linear regression, whereas univariate and multivariate logistic regression analyses were used to assess the associations between independent variables and the prevalence of OHT. The collinearity of the independent variables was assessed using the variance inflation factor (VIF), with multicollinearity defined as VIF ≥ 10. P < 0.05 was considered statistically significant. All statistical analyses were performed using IBM SPSS Statistics for Windows, version 29.0 (IBM Corp., Armonk, NY, USA).
Ethics statement
This study was approved by the Institutional Review Board of Pusan National University (approval No. PNU IRB/2024-177-HR). Because this study retrospectively analyzed data from a completed survey, the participants had no direct risks or benefits. Moreover, as the dataset did not include personal identification information, a waiver of consent was obtained and approved.
RESULTS
The analysis included 1,896 participants (816 male, 1,080 female) with a mean age of 40.60 ± 0.32 years (range, 19–59 years). The highest and lowest proportions of age groups were those 50+ (28.9%) and 30–39 (20.8%) years. The mean IOP was 15.48 ± 0.08 mmHg, and 33 participants (1.2%) had OHT. The metabolic characteristics and IOP-related variables are summarized in Table 1.
Table 1. Characteristics of the study participants (N = 1,896).
| Characteristics | Values | |
|---|---|---|
| Age, yr | 40.60 ± 0.32 | |
| 19–29 | 371 (23.9) | |
| 30–39 | 340 (20.8) | |
| 40–49 | 539 (26.4) | |
| ≥ 50 | 646 (28.9) | |
| Sex | ||
| Male | 816 (51.9) | |
| Female | 1,080 (48.1) | |
| IOP, mmHg | 15.48 ± 0.08 | |
| No. of with ocular hypertension | 33 (1.2) | |
| SBP, mmHg | 115.81 ± 0.41 | |
| DBP, mmHg | 74.15 ± 0.32 | |
| Glucose, mg/dL | 99.16 ± 0.55 | |
| Insulin, μIU/mL | 9.25 ± 0.18 | |
| HOMA-IR | 2.36 ± 0.06 | |
| BMI, kg/m2 | 24.18 ± 0.11 | |
| WC, cm | ||
| Male | 87.67 ± 0.42 | |
| Female | 77.83 ± 0.37 | |
| Triglycerides, mg/dL | 127.05 ± 2.75 | |
| HDL-C, mg/dL | ||
| Male | 48.22 ± 0.43 | |
| Female | 57.55 ± 0.47 | |
Values are presented as mean ± standard error or number (%), unless otherwise indicated.
IOP = intraocular pressure, SBP = systolic blood pressure, DBP = diastolic blood pressure, HOMA-IR = homeostasis model assessment of insulin resistance, BMI = body mass index, WC = waist circumference, HDL-C = high-density lipoprotein cholesterol.
The metabolic syndrome group (20.3%) was significantly older (45.05 ± 0.58 years) than the normal group (39.46 ± 0.37 years, P < 0.001). The prevalence of metabolic syndrome was higher in males than females (25.0% vs. 15.3%, P < 0.001). Participants with metabolic syndrome exhibited higher IOP (16.05 ± 0.15 mmHg) than those without (15.33 ± 0.09 mmHg, P < 0.001), although the prevalence of OHT did not differ significantly between the two groups (1.6% vs. 1.1%). Metabolic syndrome was associated with higher SBP and DBP, fasting glucose, fasting insulin, HOMA-IR, BMI, WC, triglycerides, and lower HDL-C levels (all P < 0.001) (Table 2).
Table 2. Comparison of characteristics between the metabolic syndrome and normal groups.
| Characteristics | Normal group (n = 1,513) | Metabolic syndrome group (n = 383) | P value | |
|---|---|---|---|---|
| Age, yr | 39.46 ± 0.37 | 45.05 ± 0.58 | < 0.001a* | |
| Sex | < 0.001b* | |||
| Male | 608 (75.0) | 208 (25.0) | ||
| Female | 905 (84.7) | 175 (15.3) | ||
| IOP, mmHg | 15.33 ± 0.09 | 16.05 ± 0.15 | < 0.001a* | |
| No. of with ocular hypertension | 24 (1.1) | 9 (1.6) | 0.527b | |
| SBP, mmHg | 112.94 ± 0.40 | 127.07 ± 0.86 | < 0.001a* | |
| DBP, mmHg | 72.17 ± 0.28 | 81.92 ± 0.63 | < 0.001a* | |
| Glucose, mg/dL | 94.85 ± 0.46 | 116.05 ± 1.73 | < 0.001a* | |
| Insulin, μIU/mL | 7.83 ± 0.12 | 14.79 ± 0.55 | < 0.001a* | |
| HOMA-IR | 1.86 ± 0.03 | 4.31 ± 0.18 | < 0.001a* | |
| BMI, kg/m2 | 23.25 ± 0.11 | 27.83 ± 0.22 | < 0.001a* | |
| WC, cm | ||||
| Male | 85.01 ± 0.42 | 95.67 ± 0.60 | < 0.001a* | |
| Female | 75.49 ± 0.33 | 90.73 ± 0.77 | < 0.001a* | |
| Triglycerides, mg/dL | 101.01 ± 1.95 | 229.08 ± 7.90 | < 0.001a* | |
| HDL-C, mg/dL | ||||
| Male | 50.60 ± 0.44 | 41.05 ± 0.74 | < 0.001a* | |
| Female | 59.58 ± 0.49 | 46.36 ± 0.90 | < 0.001a* | |
Values are presented as mean ± standard error or number (%), unless otherwise indicated.
IOP = intraocular pressure, SBP = systolic blood pressure, DBP = diastolic blood pressure, HOMA-IR = homeostasis model assessment of insulin resistance, BMI = body mass index, WC = waist circumference, HDL-C = high-density lipoprotein cholesterol.
aThe t-test, bThe χ2 test.
*P < 0.05 was considered statistically significant.
Age and sex did not differ significantly between the OHT and normal groups. However, the mean IOP was significantly higher in the OHT group (24.28 ± 0.40 mmHg) than in the normal group (15.37 ± 0.08 mmHg, P < 0.001). In males, the OHT group had a significantly larger WC (93.26 ± 2.13 cm) compared with the normal group (87.60 ± 0.43 cm, P = 0.011). The other metabolic variables did not differ significantly between the groups (Table 3).
Table 3. Comparison of characteristics between the OHT and normal groups.
| Characteristics | Normal group (n = 1,863) | OHT group (n = 33) | P value | |
|---|---|---|---|---|
| Age, yr | 40.62 ± 0.33 | 38.86 ± 2.37 | 0.464a | |
| Sex | 0.892b | |||
| Male | 803 (98.8) | 13 (1.2) | ||
| Female | 1,060 (98.8) | 20 (1.2) | ||
| IOP, mmHg | 15.37 ± 0.08 | 24.28 ± 0.40 | < 0.001a* | |
| No. of with metabolic syndrome | 374 (20.3) | 9 (26.2) | 0.527b | |
| SBP, mmHg | 115.82 ± 0.42 | 115.08 ± 2.61 | 0.782a | |
| DBP, mmHg | 74.16 ± 0.32 | 73.46 ± 1.35 | 0.614a | |
| Glucose, mg/dL | 99.15 ± 0.56 | 99.84 ± 1.69 | 0.695a | |
| Insulin, μIU/mL | 9.23 ± 0.18 | 10.83 ± 1.06 | 0.136a | |
| HOMA-IR | 2.36 ± 0.06 | 2.71 ± 0.29 | 0.226a | |
| BMI, kg/m2 | 24.18 ± 0.11 | 24.66 ± 1.15 | 0.677a | |
| WC, cm | ||||
| Male | 87.60 ± 0.43 | 93.26 ± 2.13 | 0.011a* | |
| Female | 77.86 ± 0.38 | 74.66 ± 3.16 | 0.321a | |
| Triglycerides, mg/dL | 127.11 ± 2.74 | 122.11 ± 16.85 | 0.763a | |
| HDL-C, mg/dL | ||||
| Male | 48.25 ± 0.43 | 45.64 ± 2.07 | 0.218a | |
| Female | 57.41 ± 0.48 | 69.63 ± 6.16 | 0.052a | |
Values are presented as mean ± standard error or number (%), unless otherwise indicated.
OHT = ocular hypertension, IOP = intraocular pressure, SBP = systolic blood pressure, DBP = diastolic blood pressure, HOMA-IR = homeostasis model assessment of insulin resistance, BMI = body mass index, WC = waist circumference, HDL-C = high-density lipoprotein cholesterol.
aThe t-test, bThe χ2 test.
*P < 0.05 was considered statistically significant.
Participants with hypertension, hyperglycemia, hyperinsulinemia, HOMA-IR ≥ 2.34, BMI ≥ 25 kg/m2, abdominal obesity, and hypertriglyceridemia had significantly higher IOP, with the largest differences observed for hyperinsulinemia (0.57 mmHg higher than normal). HOMA-IR ≥ 2.34 was associated with a higher prevalence of OHT (2.0% vs. 0.8%, P = 0.026) (Table 4).
Table 4. Comparison of IOP and the prevalence of OHT according to metabolic syndrome components.
| Characteristics | IOP, mmHg | P valuea | No. of patients | OHT group | P valueb | |
|---|---|---|---|---|---|---|
| Sex | 0.633 | 0.892 | ||||
| Male | 15.51 ± 0.10 | 816 | 13 (1.2) | |||
| Female | 15.45 ± 0.10 | 1,080 | 28 (1.2) | |||
| Blood pressure, mmHg | 0.014* | 0.643 | ||||
| ≥ 130/85 | 15.83 ± 0.16 | 355 | 6 (1.5) | |||
| < 130/85 | 15.40 ± 0.09 | 1,541 | 27 (1.1) | |||
| Fasting glucose, mg/dL | < 0.001* | 0.164 | ||||
| ≥ 100 | 15.84 ± 0.12 | 610 | 13 (1.7) | |||
| < 100 | 15.32 ± 0.09 | 1,286 | 20 (1.0) | |||
| Fasting insulin, μIU/mL | 0.001* | 0.129 | ||||
| ≥ 13 | 15.95 ± 0.17 | 337 | 9 (2.0) | |||
| < 13 | 15.38 ± 0.08 | 1,559 | 24 (1.0) | |||
| HOMA-IR | < 0.001* | 0.026* | ||||
| ≥ 2.34 | 15.82 ± 0.13 | 639 | 17 (2.0) | |||
| < 2.34 | 15.31 ± 0.08 | 1,257 | 16 (0.8) | |||
| BMI, kg/m2 | 0.001* | 0.301 | ||||
| ≥ 25 | 15.72 ± 0.11 | 685 | 14 (1.6) | |||
| < 25 | 15.34 ± 0.09 | 1,211 | 19 (1.0) | |||
| WC, cm | < 0.001* | 0.057 | ||||
| Male ≥ 90, Female ≥ 85 | 15.78 ± 0.12 | 569 | 15 (2.0) | |||
| Male < 90, Female < 85 | 15.35 ± 0.09 | 1,327 | 18 (0.8) | |||
| Triglycerides, mg/dL | 0.001* | 0.962 | ||||
| ≥ 150 | 15.88 ± 0.15 | 474 | 9 (1.2) | |||
| < 150 | 15.34 ± 0.09 | 1,422 | 34 (1.2) | |||
| HDL-C, mg/dL | 0.879 | 0.321 | ||||
| Male < 40, Female < 50 | 15.46 ± 0.13 | 505 | 9 (0.8) | |||
| Male ≥ 40, Female ≥ 50 | 15.49 ± 0.09 | 1,391 | 24 (1.3) | |||
Values are presented as mean ± standard error or number (%), unless otherwise indicated.
IOP = intraocular pressure, OHT = ocular hypertension, HOMA-IR = homeostasis model assessment of insulin resistance, BMI = body mass index, WC = waist circumference, HDL-C = high-density lipoprotein cholesterol.
aThe t-test, bThe χ2 test.
*P < 0.05 was considered statistically significant.
After adjusting for sex and metabolic syndrome components using multiple linear regression, hyperglycemia (coefficient = 0.29, P = 0.037) and hypertriglyceridemia (coefficient = 0.39, P = 0.027) showed significant positive associations with IOP (Table 5). Univariate logistic regression showed that insulin resistance (odds ratio [OR], 2.44; 95% confidence interval [CI], 1.08–5.48; P = 0.031) was significantly associated with OHT. In multivariate analysis adjusted for sex and metabolic factors, abdominal obesity was the only significant factor (OR, 2.90; 95% CI, 1.35–6.23; P = 0.007) (Table 6). Among metabolic syndrome factors, only abdominal obesity was significantly associated with OHT in the multivariate model (Fig. 2). Although insulin resistance and hyperglycemia showed trends toward an association, they did not reach statistical significance.
Table 5. Multiple linear regression of the associations between metabolic syndrome components and intraocular pressure using.
| Variables | Coefficient B | SE | t | P value |
|---|---|---|---|---|
| Male | −0.14 | 0.12 | −1.12 | 0.264 |
| High blood pressure | 0.22 | 0.18 | 1.18 | 0.238 |
| High fasting glucose | 0.29 | 0.14 | 2.10 | 0.037* |
| High fasting insulin | 0.25 | 0.22 | 1.14 | 0.257 |
| High HOMA-IR | 0.15 | 0.19 | 0.80 | 0.423 |
| High BMI | 0.01 | 0.18 | 0.04 | 0.965 |
| Abdominal obesity | 0.12 | 0.20 | 0.60 | 0.546 |
| High triglycerides | 0.39 | 0.18 | 2.23 | 0.027* |
| Low HDL-C | −0.32 | 0.17 | −1.87 | 0.064 |
Metabolic syndrome components; high blood pressure (systolic ≥ 130 mmHg or diastolic ≥ 85 mmHg); high fasting glucose (≥ 100 mg/dL); high fasting insulin (≥ 13 μIU/mL); high HOMA-IR (≥ 2.34); high BMI (≥ 25 kg/m2); abdominal obesity (male ≥ 90 cm, female ≥ 85 cm); high triglycerides (≥ 150 mg/dL); low HDL-C (male < 40 mg/dL, female < 50 mg/dL).
SE = standard error, HOMA-IR = homeostasis model assessment of insulin resistance, BMI = body mass index, HDL-C = high-density lipoprotein cholesterol.
*P < 0.05 was considered statistically significant.
Table 6. Univariate and multivariate logistic regression analysis of metabolic syndrome components affecting ocular hypertension.
| Variables | Univariate | Multivariate | ||
|---|---|---|---|---|
| ORs (95% CI) | P value | ORs (95% CI) | P value | |
| Male | 1.07 (0.40–2.85) | 0.893 | 0.86 (0.36–2.06) | 0.741 |
| High blood pressure | 1.29 (0.44–3.75) | 0.644 | 0.98 (0.38–2.49) | 0.963 |
| High fasting glucose | 1.72 (0.79–3.75) | 0.169 | 1.18 (0.47–2.96) | 0.724 |
| High fasting insulin | 2.02 (0.80–5.10) | 0.136 | 1.13 (0.28–4.61) | 0.865 |
| High HOMA-IR | 2.44 (1.08–5.48) | 0.031* | 2.23 (0.88–5.70) | 0.092 |
| High BMI | 1.61 (0.64–4.05) | 0.306 | 0.57 (0.28–1.16) | 0.119 |
| Abdominal obesity | 2.38 (0.95–5.97) | 0.065 | 2.90 (1.35–6.23) | 0.007** |
| High triglyceride | 1.03 (0.36–2.89) | 0.962 | 0.76 (0.27–2.10) | 0.592 |
| Low HDL-C | 0.61 (0.22–1.65) | 0.326 | 0.41 (0.14–1.21) | 0.106 |
Metabolic syndrome components; high blood pressure (systolic ≥ 130 mmHg or diastolic ≥ 85 mmHg); high fasting glucose (≥ 100 mg/dL); high fasting insulin (≥ 13 μIU/mL); high HOMA-IR (≥ 2.34); high BMI (≥ 25 kg/m2); abdominal obesity (male ≥ 90 cm, female ≥ 85 cm); high triglycerides (≥ 150 mg/dL); low HDL-C (male < 40 mg/dL, female < 50 mg/dL).
OR = odds ratio, CI = confidence interval, HOMA-IR = homeostasis model assessment of insulin resistance, BMI = body mass index, HDL-C = high-density lipoprotein cholesterol.
*P < 0.05 and **P < 0.01 were considered statistically significant.
Fig. 2. Comparison of odds ratios of metabolic syndrome components affecting ocular hypertension. Among the metabolic syndrome factors, only abdominal obesity is significantly associated with ocular hypertension. Abdominal obesity (male ≥ 90 cm, female ≥ 85 cm); insulin resistance (homeostasis model assessment of insulin resistance ≥ 2.34); glucose (≥ 100 mg/dL); insulin (≥ 13 μIU/mL); blood pressure (systolic ≥ 130 mmHg or diastolic ≥ 85 mmHg); triglycerides (≥ 150 mg/dL); body mass index (≥ 25 kg/m2); high-density lipoprotein cholesterol (male < 40 mg/dL, female < 50 mg/dL).
OR = odds ratio, CI = confidence interval.
DISCUSSION
OHT can cause mechanical stress on the lamina cribrosa, leading to retinal ganglion cell damage and visual field impairment.6,23 Metabolic syndrome is a combination of hypertension, hyperglycemia, insulin resistance, abdominal obesity, and dyslipidemia, which can lead to vascular endothelial cell dysfunction, increased systemic inflammation, and ectopic fat accumulation, which increase the risk of cardiovascular disease.24 This study explored the relationship between metabolic syndrome and IOP, as well as the prevalence of OHT. The results revealed the association between metabolic syndrome and a higher IOP, although OHT prevalence did not differ significantly between the groups. Notably, abdominal obesity exhibited the strongest association with OHT, showing a 2.90-fold increased risk compared with normal weight.
Several studies have demonstrated an association between blood pressure and IOP.25,26,27 Klein et al.25 reported that a 10 mmHg increase in SBP or DBP raised IOP by 0.21 and 0.43 mmHg, respectively. Similarly, Yasukawa et al.26 observed a 1.88-fold higher prevalence of OHT in individuals with blood pressure ≥ 140/90 mmHg. A Korean study demonstrated that blood pressure ≥ 130/85 mmHg was associated with a 0.3 mmHg increase in IOP.27 Compared with Western findings, the Korean results suggest a smaller magnitude of IOP elevation linked to hypertension. This discrepancy may be attributed to differences in genetic, environmental, or dietary factors between Korean and Western populations. Nonetheless, both Korean and Western studies have underscored the relationship between elevated blood pressure and IOP, supporting hypertension as a critical modifiable risk factor for glaucoma. The mechanism may involve increased ciliary arterial pressure, leading to elevated aqueous humor production.11 However, in the present study, although the univariate analysis indicated a higher mean IOP in participants with hypertension, this association was not statistically significant after multivariate adjustment. Furthermore, logistic regression analysis revealed no significant association between hypertension and OHT. These findings suggest that other metabolic factors may confound the relationship between hypertension and elevated IOP or OHT.
The Baltimore Eye Survey reported that the IOP is 0.4–0.6 mmHg higher in individuals with diabetes,28 while the Blue Mountains Eye Study reported a higher OHT prevalence in the diabetes group compared with the control group (6.7% vs. 3.5%).29 The osmotic influx of fluid into the eye and autonomic dysfunction caused by hyperglycemia may contribute to this relationship.28,29 The Baltimore Eye Survey primarily focused on IOP as a continuous variable, whereas the Blue Mountains Eye Study addressed OHT as a categorical outcome. Given the pathophysiological differences and distinct clinical implications of IOP elevation and OHT diagnosis, these endpoints must be distinguished when comparing epidemiological findings. In the present study, hyperglycemia was associated with a 0.52 mmHg increase in IOP in the univariate analysis. After adjusting for sex and other metabolic factors, hyperglycemia showed a significant direct association with IOP, consistent with the Baltimore Eye Survey findings.28 However, OHT prevalence did not differ significantly between the hyperglycemia and normal groups (1.7% vs. 1.0%), highlighting the need to explore additional contributing factors such as sex and age distribution.
Fasting insulin is a simple and indirect measure of insulin resistance; however, its accuracy is limited in populations with impaired glucose tolerance, where insulin resistance tends to be higher.30,31 HOMA-IR, which incorporates both fasting insulin and glucose, is considered a more reliable marker.22 Studies in Japanese and Korean populations have demonstrated a significant positive correlation between HOMA-IR levels and IOP, suggesting that insulin resistance contributes to IOP elevation.32,33 Fujiwara et al.33 reported that a one-unit increase in log HOMA-IR raised the IOP by 0.52 mmHg in males and 0.32 mmHg in females, potentially due to sympathetic nervous system activation and reduced nitric oxide availability caused by endothelial dysfunction. In the present study, univariate analysis revealed an association of higher IOP with fasting insulin levels ≥ 13 μIU/mL and HOMA-IR ≥ 2.34. However, these associations were not significant in the multivariate-adjusted model. Moreover, hyperinsulinemia did not significantly affect the prevalence of OHT. While the OHT prevalence was 2.0% among individuals with insulin resistance, compared with 0.8% in the normal group, this difference was not significant in the multivariate analysis. These findings suggest the need for further research on the association between insulin resistance and IOP.
Obesity and dyslipidemia contribute to elevated IOP through mechanisms such as intraorbital fat accumulation, increased episcleral venous pressure, and higher blood viscosity, which impair aqueous humor outflow.12 Studies have shown positive correlations between IOP and both abdominal obesity and BMI in American and Korean populations.34,35 In their meta-analysis, Wang and Bao36 reported that individuals with dyslipidemia had a 0.51 mmHg higher mean IOP, with a 0.016 mmHg increase in IOP for every 10 mg/dL rise in serum triglyceride level. In the present study, although BMI ≥ 25 kg/m2, abdominal obesity, and hypertriglyceridemia were associated with IOP increases of 0.38, 0.43, and 0.54 mmHg, respectively, in the univariate analysis, only hypertriglyceridemia remained significantly associated with IOP after adjusting for sex and other metabolic syndrome components. Moreover, serum HDL-C was not linked to elevated IOP, contrary to earlier reports of a negative correlation between HDL-C and IOP.37 Further research is needed to clarify the roles of triglycerides, HDL-C, and total cholesterol in IOP regulation.34,36
Abdominal obesity showed the strongest association with OHT in the present study, with a prevalence of 2.0% compared with 0.8% in the normal group. After adjusting for sex and other metabolic syndrome factors, abdominal obesity was associated with a 2.90-fold higher risk of OHT, highlighting its significant role in metabolic syndrome components.
Son et al.38 reported a prevalence of metabolic syndrome of 12.2% and OHT in 3.45% of participants in a Korean population. The prevalence of OHT was significantly higher in males (4.8%) than in females (1.6%). The OHT group showed significantly higher SBP and DBP, fasting glucose level, triglyceride level, and WC, and lower serum HDL-C levels compared with the normal IOP group.38 In contrast, the present study observed no significant differences in age, sex, metabolic syndrome prevalence, blood pressure, fasting glucose, triglycerides, and HDL-C levels between the OHT and normal groups. However, consistent with the findings reported by Son et al.,38 males in the OHT group had a significantly higher WC than those in the normal group. These findings emphasize the role of abdominal obesity in the risk of OHT, particularly in males.
Yi et al.27 analyzed data from the 2008–2010 KNHANES and reported a 24.5% prevalence of metabolic syndrome, slightly higher than in the present study, and a lower OHT prevalence of 0.5%. The authors found that blood pressure ≥ 130/85 mmHg increased the IOP by 0.3 mmHg in both males and females. Moreover, fasting glucose ≥ 100 mg/dL was associated with a 0.4 mmHg increase in males and a 0.5 mmHg increase in females, while low HDL-C or hypertriglyceridemia increased IOP by 0.3 mmHg in males but had no effect in females. Abdominal obesity was not linked to elevated IOP in either sex, while BMI ≥ 25 kg/m2 increased IOP by 0.5 mmHg in males and 0.4 mmHg in females. Additionally, HOMA-IR ≥ 2.34 was associated with a 0.4 and 0.3 mmHg higher IOP in males and females, respectively. Finally, logistic regression revealed that age, BMI, hypertension, hyperglycemia, and hypertriglyceridemia were significant OHT risk factors in males, with hypertension being the most strongly associated risk factor, whereas only hyperglycemia was a significant risk factor in females.
While the findings of the present study were partially consistent with those reported by Yi et al.,27 only hyperglycemia and hypertriglyceridemia showed significant direct associations with IOP after multivariate adjustment. Other factors such as hypertension, HOMA-IR ≥ 2.34, and BMI ≥ 25 kg/m2 did not retain statistical significance in our adjusted model. However, unlike Yi et al.,27 the present study demonstrated a significant association between abdominal obesity and OHT, with a 2.90-fold increase in risk after adjusting for other metabolic factors. These results highlight the importance of addressing abdominal obesity when managing OHT risk in patients with metabolic syndrome.
This study has some limitations. As a retrospective cross-sectional survey, it identified associations between OHT and metabolic syndrome but could not establish causality. Additionally, the study population was limited to individuals aged 19–59 years, with no data from older adults (≥ 65 years) who may have different risk profiles. Future research should include longitudinal studies to confirm causality and incorporate older populations to provide a more comprehensive understanding of OHT risk across all age groups. Furthermore, this study was a part of a national health survey targeting participants from Korea. Due to the limited scope of the participants, the research results may have limited generalizability. Moreover, glaucoma prevalence differs across ethnicities, with a particularly high prevalence of angle-closure glaucoma in Asia,39,40 whereas high myopia, which is related to the development of glaucomatous optic neuropathy, is more common in East Asia.41 Additionally, the criteria for WC in metabolic syndrome and the prevalence of metabolic syndrome vary by ethnicity.42 Therefore, further studies including diverse countries and ethnicities are needed. Despite these limitations, the results of the present study demonstrated a clear association between metabolic syndrome and OHT in the Korean population. Among the components of metabolic syndrome, abdominal obesity emerged as the strongest risk factor for OHT. With the increasing prevalence of metabolic syndrome owing to dietary and lifestyle changes in Korea, targeted interventions for managing abdominal obesity and other metabolic risk factors are crucial.27
Future studies should explore the mechanisms underlying the relationships between specific metabolic factors and OHT, such as the roles of lipoproteins and systemic inflammation. Research should also focus on identifying effective prevention strategies, including lifestyle modifications and regular screening programs, to reduce OHT risk and prevent glaucoma progression. Expanding studies to include diverse populations and integrating genetic, environmental, and behavioral factors could further enhance our understanding and inform tailored approaches for OHT prevention and management.
Footnotes
Disclosure: The authors have no potential conflicts of interest to disclose.
Data Availability Statement: Data supporting the findings of this study are available from the 2021 National Nutrition and Health Examination Survey (https://knhanes.kdca.go.kr).
- Conceptualization: Lee JY, Lee JS.
- Data curation: Lee JY, Cho MH.
- Formal analysis: Lee JY, Lee J.
- Methodology: Lee J, Lee JS.
- Visualization: Lee JY, Cho MH.
- Writing - original draft: Lee JY.
- Writing - review & editing: Cho MH, Lee J, Lee JS.
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