elevated intraocular pressure is associated with insulin resistance and metabolic syndrome

DIABETES/METABOLISM RESEARCH AND REVIEWS R E S E A R C H A R T I C L EDiabetes Metab Res Rev 2005; 21: 434–440.Published online 13 January 2005 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/dmrr.529

Elevated intraocular pressure is associated withinsulin resistance and metabolic syndrome

Sang Woo Oh,1*Sangyeoup Lee2

Cheolyoung Park3

Dong Jun Kim4

1Department of Family Medicine andCenter for Health Promotion,Ilsan-paik Hospital, Inje University,College of Medicine, Gyeonggi-Do,South Korea2Department of Family Medicine,Pusan National University, College ofMedicine, Busan, South Korea3Department of Internal Medicine,Hallym Sacred Heart Hospital,Hallym University, College ofMedicine, Gyeonggi-Do, South Korea4Department of Internal Medicine,Ilsan-paik Hospital, Inje University,College of Medicine, Gyeonggi-Do,South Korea

*Correspondence to: Sang Woo Oh,Departments of Family Medicineand Health Promotion Center, InjeUniversity Ilsan-paik Hospital,Daewha-Dong, Ilsan-Gu, Goyang-Si,Gyeonggi-Do, 411–706,(South) Korea. E-mail:[email protected],[email protected]

Received: 24 August 2004Accepted: 18 November 2004

Abstract

Background and aim Elevated intraocular pressure (IOP), a well-knownrisk factor for glaucoma, has recently been shown to be associated with somemetabolic complications and obesity. We investigated the link between IOPand metabolic disturbances, focusing especially on metabolic syndrome andinsulin resistance.

Methods Eye examinations, including IOP measurement, were conductedon 943 subjects (533 men and 410 women). Body mass index (BMI),percent body fat, waist circumference, systolic and diastolic pressure, fastinginsulin, glucose, lipids, and other metabolic parameters were measured.The homeostasis model assessment (HOMA) score and McAuley index werecalculated to assess whole-body insulin resistance.

Results Both of these insulin resistance indices showed positive associationswith IOP (p < 0.05), even after statistical adjustment for other risk factors.IOP was higher in participants with metabolic syndrome, as compared tothose who did not have metabolic syndrome. The mean IOP tended toincrease linearly with the presence of increasing numbers of components formetabolic syndrome.

Conclusions These results suggest that insulin resistance might contributeto an explanation that would account for many previous findings concerningthe association between IOP and obesity, hypertension, and diabetes.Copyright 2005 John Wiley & Sons, Ltd.

Keywords intraocular pressure; insulin resistance; metabolic syndrome

Introduction

Glaucoma is a progressive optic nerve disease often associated with elevatedintraocular pressure (IOP), and characterized by optic disc cupping and visualfield defects. Because glaucoma, especially open angle glaucoma (OAG), is aleading cause of blindness and visual loss [1], many studies have attempted toidentify the associated risk factors [2–12]. Among the identified risk factors,IOP has received greater attention because it is a major and modifiable riskfactor for glaucoma [13–19]. Moreover, recent evidence has demonstratedthat lowering the IOP can reduce the risk of glaucoma in individuals withelevated IOP [20,21].

Although the underlying mechanisms remain unclear, many previousstudies have shown that elevated IOP is clearly associated with severalhealth problems, such as hypertension [2,5–9], diabetes [5,7,10–14], andother metabolic disturbances. Interestingly, recent studies have reported

Copyright 2005 John Wiley & Sons, Ltd.

Ocular Pressure and Insulin Resistance 435

that obesity could be an important modifiable risk factorfor elevated IOP [3–5,22–24]. Obesity is also regarded asan important risk factor for diabetes, hypertension, andother metabolic disturbances. The association of obesitywith each of these complications is known to have acommon pathophysiologic mechanism: insulin resistance[25].

In light of these prior findings, a positive link betweeninsulin resistance and elevated IOP may be expected.However, the possible effect of insulin resistance onIOP has not yet been clarified, and data addressingthis relationship are scarce. Therefore, we conducted thisstudy to evaluate the association of insulin resistance withIOP, and, additionally, to assess the relationship betweenIOP and metabolic syndrome.

Subjects and methods

Study subjects

We examined 1213 healthy visitors to the Health Pro-motion Center at Inje University Ilsan-Paik Hospital, inSouth Korea, from 1 February to 31 December, 2003.Before the testing, the medical histories and conditionsof the participants were carefully ascertained by physi-cians including ophthalmologist. Informed consent wasobtained from participating patients. Participants receiv-ing medical treatment for hypertension, diabetes mellitus,hypercholesterolemia, glaucoma, including open angleand angle closure glaucoma, or other ophthalmologicdiseases that can influence IOP were excluded from thestudy (n = 270). Finally, 943 subjects were included inthis study.

Ophthalmologic examination

The ophthalmologic examinations of all participantsincluded best-corrected visual acuity by the Snellenchart, IOP measurements by noncontact tonometry (CT-60, Topcon, Japan), and fundus examination withphotography (TRC-NW5, Topcon, Japan). The IOP wasmeasured on the center of cornea, three consecutivetimes, and the mean value for each eye was calculated.If the mean values for the two eyes differed by morethan 3.0 mmHg, the measurements were repeated forboth eyes. All measurements were conducted between8 and 10 A.M. to minimize the effect of diurnalvariation. All participants with decreased visual acuity,abnormal fundus findings such as elevated C/D ratioor retinopathy, IOP elevated above 21 mmHg, or otherabnormalities found during the initial examinations, wereretested using Humphrey automated perimetry and othertesting procedures to exclude the possibility of overtglaucoma. Participants with diabetic, hypertensive, orother retinopathy, which could contribute to elevatedIOP, were also excluded from the study.

Assessments of adiposity and clinicalmeasurements

Height and body weight were measured using a digitalscale, with the examinee wearing a light gown. Waistcircumference was determined to the nearest 0.1 cm usinga tape measure at the midpoint between the lower costalmargin and the iliac crest by well-trained examiners.Total body fat percent was assessed using a bioelectricalimpedance analyzer (Inbody 3.0; Biospace, Korea). Thesubjects were asked to fast for 12 h and consume noliquids for 2 h before the measurement session.

Resting blood pressure was assessed using an automaticsphygmomanometer (Colin, Japan) in the morning(between 8 to 10 A.M.). Blood samples were obtainedfrom each participant’s antecubital vein after a 12-hfast to determine plasma glucose, serum insulin, andlipids. Plasma glucose levels were measured enzymatically(glucose oxidase) within 4 h of sample collection usingthe Advia 1650 chemistry system (Bayer, Japan). Serumtotal cholesterol, HDL cholesterol, and triglycerides weredetermined enzymatically with the same instrument.LDL cholesterol was calculated using the Friedewaldformula [26]. The serum insulin levels were determinedwith a commercial radioimmunoassay (COAT-A-Count,Diagnostic Products, Los Angeles, CA, USA).

Assessments of insulin resistanceindices

The homeostasis model assessment for insulin resistance(HOMA-IR) and the McAuley index were used asestimators of insulin resistance and sensitivity. TheHOMA-IR was calculated from the formula describedby Matthews et al. [27]: (fasting insulin in µU/mL ×fasting glucose in mmol/L)/22.5. The index developedby McAuley et al. for quantification of peripheral insulinsensitivity [28], based on the levels of the triglycerides andinsulin, was calculated using the equation: exp[2.63–0.28ln(insulin in µU/mL) -0.31 ln(triglycerides in mmol/L)].

Because the underlying pathophysiologic mechanism ofmetabolic syndrome is insulin resistance, and metabolicsyndrome also can be regarded as an indicator ofinsulin resistance [29], we additionally analyzed therelationship between metabolic syndrome and IOP. Weused the definition of metabolic syndrome suggestedby the National Cholesterol Education Program AdultTreatment Panel III (NCEP ATP III) from the USA[30], in which metabolic syndrome was defined ashaving three or more of the following five risk factors:(1) abdominal obesity: waist circumference >102 cm formen and >94 cm for women, (2) serum triglycerides ≥150 mg/dL, (3) serum HDL-cholesterol <40 mg/dL formen and <50 mg/dL for women, (4) systolic/diastolicblood pressure ≥ 130/85 mmHg, and (5) fasting plasmaglucose ≥ 110 mg/dL. Because Asians have a greaterrisk of fitting the metabolic profile with a lower waistcircumferences (WC), as compared to Caucasians [31],

Copyright 2005 John Wiley & Sons, Ltd. Diabetes Metab Res Rev 2005; 21: 434–440.

436 S. W. Oh et al.

the cut-off point for abdominal obesity described above(WC >102 cm for males, >94 cm for females) does notapply to Asians. Therefore, we used the definition ofabdominal obesity suggested by the 1998 WHO AsianPacific Guidelines (WC >90 cm for males, >80 cm forfemales) [31] as the cutoff point for increased risk.

Statistical analyses

We log-transformed the positively skewed distributionsof the insulin and triglycerides levels, and the HOMA-IR. We then inverse-transformed the log means andstandard error of means (SEM) to report means ± SEM.We used the unpaired Student’s t -test to compare anumber of characteristics found in the male and femaleparticipants. On the basis of the definition of eachcomponent of metabolic syndrome, we classified thesubjects into two groups, according to whether theyhave the component of metabolic syndrome or not.We then compared the IOP levels in the two groupsusing the unpaired Student’s t -test. We also categorizedthe study subjects by the numbers of components ofmetabolic syndrome they have. One-way analysis ofvariance (ANOVA) followed by Tukey’s post hoc test wasused to determine the significance of differences betweenthe mean values of these groups. For significant ANOVAresults, polynomial contrast was applied, to test for lineartrends.

We also used multiple linear regression models toadjust potential covariates. Several models were testedthat included some or all of the following factors:sex, age, weight, height, BMI, waist circumference,body fat percent, blood pressure, LDL cholesterol, HDLcholesterol, log triglycerides, log fasting insulin, and/orthe insulin sensitivity indices previously mentioned. Astepwise method was used to select the appropriate

model. The p−value of the covariates was set at 0.05for inclusion in the regression model and at 0.10 forremoval. Stepwise regression objectively selected thevariables according to their predictive power. Unlessotherwise indicated, the data were reported as mean± SEM. The level of significance was chosen as p−values<0.05. All statistical analysis was conducted usingSPSS, version 11.0.1 for Windows (SPSS, Chicago,USA).

Results

The characteristics of the study subjects are summarizedin Table 1. Statistically significant differences betweenmen and women were found with respect to the followingvariables: age, height, weight, BMI, waist circumference,body fat percent, systolic and diastolic blood pressure,fasting glucose level, HDL cholesterol, log triglycerides,log HOMA-IR, McAuley index, and intraocular pressure.However, total cholesterol, LDL cholesterol, and loginsulin did not show any significant differences. Becausethere were significant differences (p for interaction <0.05)between the genders, affecting the interaction of manyvariables, especially the insulin resistance indices, wepresented the results separately, by gender.

Analysis of the components of metabolic syndromeshowed that both men and women with high bloodpressure, high fasting glucose, or metabolic syndromehad significantly higher IOP levels, as compared tosubjects without these risk factors (Table 2, all p−values<0.05). Men with abdominal obesity and women withhigh triglyceride also had elevated IOP levels (p−values<0.05). Aside from these factors, no other statisticallysignificant differences in IOP were found for men orwomen, in comparisons based on the presence of thefactors listed in Table 2.

Table 1. Clinical characteristics of the study subjects

Variables Men (n = 533) Women (n = 410) p-value

Age (years) 44.8 ± 0.5 47.1 ± 0.6 0.002Height (cm) 169.4 ± 0.3 156.7 ± 0.3 <0.001Weight (kg) 71.0 ± 0.4 57.6 ± 0.4 <0.001BMI (kg/m2) 24.7 ± 0.1 23.5 ± 0.2 <0.001Waist circumference (cm) 87.0 ± 0.4 82.0 ± 0.5 <0.001Percent body fat (%) 19.4 ± 0.2 26.8 ± 0.3 <0.001Systolic BP (mmHg) 123.4 ± 0.6 118.7 ± 0.9 <0.001Diastolic BP (mmHg) 76.1 ± 0.4 72.4 ± 0.6 <0.001Fasting glucose (mg/dL) 103.3 ± 1.1 98.4 ± 1.0 0.001Total cholesterol (mg/dL) 203.6 ± 1.6 201.0 ± 1.9 0.300HDL cholesterol (mg/dL) 49.7 ± 0.5 55.1 ± 0.6 <0.001LDL cholesterol (mg/dL) 124.0 ± 1.4 121.8 ± 1.7 0.306Triglycerides (mg/dL)a 132.7 ± 1.0 106.0 ± 1.1 <0.001Insulin (µU/mL)a 9.5 ± 1.0 9.3 ± 1.0 0.428HOMA-IRa 2.4 ± 1.0 2.2 ± 1.1 0.008McAuley index 6.6 ± 0.1 7.1 ± 0.1 <0.001Right IOP (mmHg) 15.7 ± 0.1 15.2 ± 0.1 0.005Left IOP (mmHg) 15.7 ± 0.1 15.0 ± 0.1 <0.001Mean IOP (mmHg) 15.7 ± 0.1 15.1 ± 0.1 0.001

Data are means ± SEM, unless otherwise indicated.aLog-transformed to improve normality; reported as inverse-transformed log means andSEMs.



Table 2. Comparison of mean values of intraocular pressure according to each component ofmetabolic syndrome

Men Women

n Mean ± SEM p-value n Mean ± SEM p-value

Abdominal obesityNo 358 15.5 ± 0.2 0.047 180 15.0 ± 0.2 0.362Yes 175 16.1 ± 0.2 230 15.2 ± 0.2

High blood pressureNo 347 15.4 ± 0.2 <0.001 295 14.7 ± 0.2 <0.001Yes 186 16.4 ± 0.2 115 16.0 ± 0.2

High fasting glucoseNo 442 15.5 ± 0.1 0.003 365 14.9 ± 0.1 0.001Yes 91 16.5 ± 0.3 45 16.6 ± 0.5

Low HDL cholesterolNo 453 15.7 ± 0.1 0.84 263 15.0 ± 0.2 0.469Yes 80 15.8 ± 0.3 147 15.2 ± 0.2

High triglyceridesNo 313 15.5 ± 0.2 0.102 313 15.0 ± 0.1 0.037Yes 220 16.0 ± 0.2 97 15.6 ± 0.3

Metabolic syndromeNo 426 15.6 ± 0.1 0.025 319 14.9 ± 0.1 <0.001Yes 107 16.3 ± 0.3 91 16.0 ± 0.3

The definitions of the component risk factors were as follows: abdominal obesity (waist circumference of men>90 cm, women >80 cm); high blood pressure (systolic/diastolic ≥ 130/ ≥ 85 mmHg); high fasting glucose (≥ 110mg/dL); low HDL cholesterol (HDL cholesterol of men <40 mg/dL, women <50 mg/dL); and high triglycerides(≥ 150 mg/dL).

(a) (b)

(c) (d)

Figure 1. Mean intraocular pressure (IOP) according to (a) number of metabolic syndrome components presents; (b) number ofmetabolic syndrome components present, excluding high blood pressure (≥ 130/85 mmHg); (c) number of components presentexcluding high fasting glucose (≥ 110 mg/dL) and (d) number of components present, excluding both high blood pressure(≥ 130/85 mmHg) and high fasting glucose(≥ 110 mg/dL). Means labeled with letters (a, b, c, d) had statistically significantdifferences in post hoc test (p < 0.05)


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Table 3. Multiple linear regression analyses with mean intraocular pressure as outcome variable

Men Women

Model I Model II Model III Model IV

β ± SE Beta p β ± SE Beta p β ± SE Beta p β ± SE Beta p

IRa −0.22 ± 0.10 −0.09 0.037 1.37 ± 0.36 0.16 <0.001 −0.22 ± 0.11 −0.11 0.039 2.22 ± 0.48 0.247 <0.001SBP 0.03 ± 0.01 0.13 0.003 0.03 ± 0.01 0.13 0.002 0.04 ± 0.01 0.26 <0.001 0.04 ± 0.01 0.257 <0.001FBS 0.01 ± 0.01 0.10 0.022 0.02 ± 0.01 0.17 0.001Age −0.03 ± 0.01 −0.14 0.012

β, unstandardized coefficient; Beta, standardized coefficient.aThe McAuley Index was used as an insulin sensitivity and resistance index in Models I and III; Log HOMA score was used in Models II and IV.Other variables that were examined but that did not have statistically significant associations with the IOP included age (p = 0.249) and BMI(P = 0.345) in Model I; age (p = 0.261), log TG (p = 0.510), and BMI (P = 0.447) in Model II; BMI (p = 0.056) in Model III; and age (p = 0.068), logTG (p = 0.568), and BMI (p = 0.065) in Model IV.

We found that subjects with metabolic syndromehad significantly higher IOP levels than did thosewithout metabolic syndrome (Table 2). Our resultsalso showed that subjects with more components ofmetabolic syndrome had higher IOP (p for linear trend<0.001, Figure 1a); this was found for both men andwomen. Because subjects having more components ofmetabolic syndrome were more likely to have highblood pressure or high fasting glucose levels, whichalso showed significant associations with IOP in bothmen and women (Table 2), we additionally evaluatedthe linear trends between IOP and the remainingcomponents of metabolic syndrome after exclusion ofthese two components, and found significant lineartrends in these analyses (Figure 1b,c). When these twocomponents were simultaneously excluded (Figure 1d),a statistically significant linear trend was found formen (p for linear trend = 0.039), but not for women(p for linear trend = 0.072). However, even thoughno linear trend was found for women, our post hocanalysis showed that women who had three componentsof metabolic syndrome showed significantly higher IOPlevels than did those who had less than two components(Figure 1d.).

Because the correlation coefficients between systolicand diastolic blood pressure (r = 0.817 in men and0.874 in women, p < 0.001, not shown in table), andbetween anthropometric indices, such as body mass index(BMI), waist circumference and others, were so high (thelowest correlation coefficient among these was >0.695,and all p < 0.001 for both genders, not shown in table),we included these variables separately in multiple linearregression models (Table 3). Although some variationswere observed among the regression models, systolicblood pressure and insulin resistance indices showedconsistent statistical significance, regardless of the modelselected. However, age showed a significantly negativeassociation with IOP only in women, when the McAuleyindex was used as the insulin resistance index. None of theanthropometric indices showed significant associations inany of the models that included insulin resistance indicesas covariates.

Discussion

Previous studies have reported that elevated IOPwas associated with elevated blood pressure [2,5–9],elevated fasting blood glucose levels [5,7,10–14], andpossibly with obesity [3–5,22–24]. Because obesity isa strong risk factor for diabetes and hypertension,and because insulin resistance is supposed to mediatethese associations [25], an association between insulinresistance and IOP has been assumed. In this study, wefound significant associations between insulin resistanceand IOP, even after controlling for other confoundingvariables.

Our analyses used two insulin resistance indices: theHOMA-IR and the McAuley index. The HOMA-IR isan index of insulin resistance, widely used in manyepidemiologic and clinical investigations, which can becalculated from fasting insulin and glucose levels [27].Because many previous studies have reported that anincreased fasting glucose level is a risk factor for elevatedIOP, and because our regression model also revealedthis association, we additionally used another insulinresistance index, the McAuley index, in which the fastingtriglycerides level is used instead of the fasting glucoselevel [28]. Regardless of the index chosen, the IOPwas significantly associated with the degree of insulinresistance.

Previous studies in both Western and Asian populationshave reported positive associations between BMI andIOP, even after controlling for potentially confoundingvariables. Some explanations for the association betweenobesity and IOP have been proposed in previous studies[3,32,33]. Obesity has been suspected of increasing IOPby excessive intraorbital adipose tissue, increases inblood viscosity, increases in episcleral venous pressure,and a consequent decrease in the facility of aqueousoutflow. In our analysis, BMI was shown to have astatistically significant association with IOP only in modelsexcluding insulin resistance indices as covariate (notshown in table), but not in models that included theseindices. These findings suggest the possibility that therelationship between BMI and IOP might be a reflectionof the accompanying phenomenon of a true association



between insulin resistance and IOP. Insulin resistancemight therefore have contributed to previous findingsconcerning associations between IOP and other riskfactors; however, further studies are needed to elucidatethis possibility more clearly.

Previous studies have reported somewhat inconsistentresults about the relationship between IOP and othervariables. Most studies from western countries havereported a positive association between age and IOP[4–6,17], while a negative association was observedin Japanese studies [3,32]. However, in a Pakistanipopulation sample, the IOP progressively increased withage, as found in western populations [32]. Our findingsshowed almost no association of IOP with age, in mostof the multivariate analytic models, with the exception ofmodel III, in Table 3. These paradoxical results seem tobe difficult to explain; further consideration of the effectsof other putative factors will be needed in future studies.

While a weak association, at most, between IOPwith aging was found, blood pressure showed a strongassociation with IOP. Previous studies have reported aconsistent positive relationship between systolic bloodpressure and IOP [2,5–9]. However, the findingsconcerning the relationship between diastolic bloodpressure and IOP have been somewhat inconsistent[9]. Our results showed significant relationships of bothsystolic blood pressure and diastolic blood pressure withIOP, even after controlling for many confounding factors.Possible underlying mechanisms that might explain thisassociation have been suggested in previous studies, butthe evidence remains unclear. One proposed mechanismpostulates that increased blood pressure leads to anincreased filtration fraction of the aqueous humor throughelevated ciliary artery pressure, resulting in elevatedIOP [3,33,34]. Others have proposed roles for increasedsympathetic tone and serum corticosteroids [35].

The fasting blood glucose level also showed a significantpositive association with IOP; however, the etiologic linkfor these two conditions remains unclear. The autonomicdysfunction in diabetes [36], and the osmotic gradientinduced by elevated blood glucose with a consequentfluid shift into the intraocular space, have been proposedas a way to explain this association [11]; however, furtherstudies are needed to elucidate these possibilities [34].

As the number of risk factors for metabolic syndromebeing present increased, the mean IOP value tendedto increase linearly. However, this association mighthave been related to the increased chance of highfasting blood glucose levels or high blood pressurein those subjects with higher numbers of metabolicsyndrome components. Thus, we conducted additionalanalyses, excluding these two components, and still foundsignificant associations with IOP (Figure 1). All of thesefindings support a putative association between insulinresistance and elevated IOP.

Although this study showed an association betweeninsulin resistance and elevated IOP, the cross-sectionaldesign of this study limited its ability to establish acausal relationship between these conditions. Thus, we

could not provide a definite explanation of how insulinresistance is related to the development of increased IOPand glaucoma. Some other putative confounders such asexercise, which counteracts the components of metabolicsyndrome and lowers IOP [37], are not considered inthis study. This drawback with respect to cross-sectionaldesigns points to the necessity of conducting cohortstudies, or clinical trials, in order to address this issue;such studies and trials would help us determine the exactcausal relationships between the identified risk factorsand IOP.

Acknowledgements

We wish to thank the members of the Health Promotion Centerand department of ophthalmology in Ilsan-paik Hospital inKorea. We are indebted to Yun Jun Yang, MD, Eon Sook Lee,MD, Yeong Sook Yoon, MD, and Sung-Ho Beck, MD for theirassistance and cooperation.

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elevated intraocular pressure is associated with insulin resistance and metabolic syndrome

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