Current Eye Research, Early Online, 1–10, 2014 ! Informa Healthcare USA, Inc. ISSN: 0271-3683 print / 1460-2202 online DOI: 10.3109/02713683.2014.975367
RESEARCH REPORT
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Relationship Between Intraocular Pressure and Parameters of Obesity in Korean Adults: The 2008–2010 Korea National Health and Nutrition Examination Survey Hyung-Deok Jang1*, Do Hoon Kim1*, Kyungdo Han2, Suk Gyu Ha3, Yang Hyun Kim1, Jae Woo Kim1, Ji Young Park1, Su Jung Yoon1, Dong Wook Jung1, Sang Woon Park1 and Ga Eun Nam1 1
Department of Family Medicine, Korea University College of Medicine, Seoul, South Korea, 2Department of Biostatistics, Catholic University College of Medicine, Seoul, South Korea, and 3Department of Ophthalmology, Korea University College of Medicine, Seoul, South Korea
ABSTRACT Purpose: To examine the associations of various parameters of obesity including adiposity with intraocular pressure (IOP) using nationally representative data of South Korean adults. Material and methods: This cross-sectional study analyzed the data from the 2008–2010 Korea National Health and Nutrition Examination Survey. A total of 15,271 subjects (6600 men and 8671 women) participated. Body mass index (BMI), waist circumference (WC), total body fat mass, and total and regional body fat percentage were measured as parameters of obesity. Results: IOP showed positive linear associations with BMI, WC, total fat mass, and total and regional body fat percentages in men, and with BMI, WC, total fat mass, and trunk fat percentage in women after adjusting for confounding variables. Men with higher BMI, WC, total fat mass, and total and regional body fat percentages exhibited increasing trends in odd ratios for having IOP 18 mmHg after adjusting for all confounding factors (p for trend 50.001 for BMI and total fat mass; p for trend = 0.038 for WC; 0.003 for total body fat percentage; 0.002 for trunk fat percentage; 0.004 for leg fat percentage). However, only BMI showed a significantly increasing trend in the risk of IOP 18 mmHg in women. Conclusions: In addition to BMI, WC and total fat mass, total and regional body fat percentage in men and trunk fat percentage in women are positively associated with IOP. Increased BMI, WC, and total and regional body fat are positively associated with a risk of higher IOP (IOP 18 mmHg), especially in Korean men. Keywords: Adiposity, body mass index, intraocular pressure, obesity, waist circumference
acquired blindness.1,2 Many studies aiming to identify related risk factors have been performed. Elevated intraocular pressure (IOP) is a major risk factor for glaucoma that can be treated.3–5 Moreover, reducing IOP lowers the risk of glaucoma.6 Therefore, early
INTRODUCTION Glaucoma is an ocular disorder featured by the ongoing loss of retinal ganglion cells and their axons; it is one of the most common causes of
Received 7 March 2014; revised 24 September 2014; accepted 5 October 2014; published online 6 November 2014 *These authors contributed equally to this work. Correspondence: Ga Eun Nam, MD, MSc, Department of Family Medicine, Korea University Ansan Hospital, Korea University College of Medicine, 516 Gojan-dong, Danwon-gu, Ansan-si, Gyeonggi-do, 425-707, South Korea. Tel: 82-31-412-5360. Fax: 82-31-412-5364. E-mail: [email protected]
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detection and modification of risk factors for elevated IOP may be important for preventing glaucoma in a healthcare perspective. From several evidences, elevated IOP appears to be associated with cardiovascular risk factors, such as obesity and components of metabolic syndrome including abdominal obesity, high blood pressure, hyperglycemia, and abnormal levels of serum lipid profiles.7–9 The prevalence of obesity has increased rapidly worldwide including South Korea. Although the increasing prevalence of obesity has stabilized over the past decade, obesity-related diseases including diabetes, hypertension, dyslipidemia, chronic kidney disease, cardiovascular disease, and cancer remain prevalent and lead to great burdens on society.10–14 Several studies have investigated the relationship between obesity and IOP, and showed inconsistent results with respect to age, sex, ethnicity and measurement of obesity.15–19 Moreover, most of these studies used only body mass index (BMI) or waist circumference (WC) as measurements of obesity. Although these measurements are the most popular tools for evaluating obesity because of their simplicity and low cost, they might not precisely correspond to the actual degree of adiposity. Thus, direct assessment of adiposity might be a better index of obesity and more valuable tool for understanding the relationship between adiposity and metabolic changes because it is better associated with metabolic and cardiovascular risk factors.20 Dual-energy X-ray absorptiometry (DEXA) accurately detects adiposity and provides information about the total and regional percentages of fat.21 However, no study has explored directly the association between increased IOP and adiposity. Therefore, we investigated the associations of various parameters of obesity, including total body fat mass and total and regional body fat percentages, with IOP using nationally representative data of South Korean adults.
the basis of household registries using a stratified, multistage and probability-based sampling design with proportional allocation. A total of 17,446 subjects aged 19 years or older underwent eye examinations in the 2008–2010 KNHANES. We excluded subjects with any missing data from analyses. Subjects who had suffered from any cancer, chronic hepatitis or cirrhosis; those with a history of chronic kidney disease; or those with a glomerular filtration rate less than 30 mL/min/1.73 m2 were also excluded. We also excluded subjects who had been diagnosed with glaucoma from physician based on the self-reported questionnaire. Finally, 15,271 subjects (6600 men and 8671 women) were included in the analyses. All participants in the survey signed an informed consent form prior to participation. This survey was reviewed and approved by the Institutional Review Board of the KCDC.
Lifestyle Variables All subjects were asked about their lifestyle characteristics including alcohol consumption, smoking status and physical activity. Based on their average alcohol intake per day in the month before the interview, subjects were categorized as non-drinkers, light to moderate drinkers (1–30 g/day) or heavy drinkers (430 g/day).22 Subjects were subdivided into non-smokers, ex-smokers and current smokers according to their answers on the self-reported questionnaire. On the basis of their responses to the International Physical Activity Questionnaire, subjects were considered regular physical exercisers if they performed moderate exercise more than five times per week for over 30 min per session or performed vigorous exercise more than three times per week for over 20 min per session.23
Anthropometric Measurements and Body Composition Analyses MATERIALS AND METHODS Survey Overview and Study Subjects The data obtained from the 2008–2010 Korea National Health and Nutrition Examination Survey (KNHANES) were analyzed in this study. The KNHANES is designed to accurately assess the health and nutritional status of the non-institutionalized civilian population of Korea and has been conducted annually since 1998 by the Division of Chronic Disease Surveillance, Korea Centers for Disease Control and Prevention (KCDC). It comprises three surveys: health interview, health examination and nutrition examination surveys. Participants are selected from sampling household units defined on
Specially trained examiners performed anthropometric measurements of the subjects. Body weight and height were measured with the subjects being barefoot and wearing light clothing and BMI was calculated using the formula: body weight (kg)/ height2 (m2). WC was measured at the midpoint between the lower border of the rib cage and the iliac crest while subjects were standing. Obesity was defined as a BMI 25 kg/m2 and overweight as 23 BMI525 kg/m2.24,25 The cutoff points for abdominal obesity were defined as WC 90 cm for men and WC 85 cm for women.26 Total and regional (i.e. trunk and leg) body fat mass and lean mass were measured using whole-body DEXA (QDR 4500A fan-beam densitometer, Hologic Current Eye Research
Intraocular Pressure and Parameters of Obesity Inc., Bedford, MA) by qualified technicians according to manufacturer’s acquisition procedures. The results of the DEXA were analyzed using industry standard techniques at the Korean Society of Osteoporosis with Hologic Discovery Software (version 13.1, Hologic Inc., Bedford, MA). Total body fat percentage was calculated as follows: total body fat mass/total body mass (fat mass + lean mass + total body mineral content) 100. Trunk fat percentage was measured as follows: (trunk fat mass/trunk mass) 100. Leg fat percentage was measured as follows: (leg fat mass/ leg mass) 100.27,28
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relationships between parameters of obesity and IOP. After the subjects were divided into quartiles for each parameters of obesity, the differences in the prevalence of high IOP with respect to the quartiles of obesity parameters were examined. Odds ratios (ORs) and 95% confidence intervals (CIs) for having IOP 18 mmHg according to obesity parameters were obtained by hierarchical multivariate logistic regression analyses. Univariate analyses were performed in model 1. Model 2 was adjusted for age, alcohol consumption, smoking status and physical activity. Finally, model 3 was adjusted for all variables in model 2 plus hypertension and diabetes.
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RESULTS Complete ophthalmologic examinations were performed by study ophthalmologists. A visual acuity was measured using an international standard vision chart (Jin’s Vision Chart; Seoul, Korea) at a distance of 4 m. Refractive errors were measured using an autorefractor-keratometer (KR8800; Topcon, Tokyo, Japan). A slit lamp examination including assessment of peripheral anterior chamber depth by the Van Herick method (Haag-Streit model BQ-900; HaagStreit AG, Koeniz, Switzerland) was performed. Fundus photographs were taken with a digital nonmydriatic fundus camera (TRC-NW6S; Topcon, Tokyo, Japan, and Nikon D-80 digital camera; Nikon, Tokyo, Japan). IOP was measured with a Goldmann applanation tonometer (GAT; Haag-Streit; Haag-Streit AG, Koeniz, Switzerland) once for each eye from right to left. Mean values from both eyes were used in the analyses because IOP in the right and left eyes was highly correlated (Pearson correlation coefficient (r) = 0.79, p50.001). We categorized as IOP 518 mmHg or IOP 18 mmHg.29
Statistical Analysis This survey was technically supported by 30 expert committees and quality control of the survey was verified based on the consensus of committee members. All the variable data were entered into a password-protected Microsoft Office Access database and imaging data were retrieved directly from the imaging equipment and stored in their respective computers.30,31 SAS version 9.2 for Windows (SAS institute, Cary, NC) was used for all statistical analyses. Two-sided p values 50.05 were considered statistically significant. Data are presented as mean ± standard error (SE) or as percentage (SE). Student’s t-test and Chi-square test for continuous and categorical variables, respectively, were used to compare the means of baseline characteristics between sexes. Multivariate adjusted linear regression analyses were used to examine the !
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Baseline Characteristics The baseline characteristics of study subjects are presented in Table 1. The mean age was 44.4 ± 0.3 years and the proportion of male subjects was 43.2%. The mean IOP level for the whole sample was 14.0 ± 0.1 mmHg. All baseline measurements were significantly different by gender. The mean IOP in men and women was 14.1 ± 0.1 and 13.8 ± 0.1 mmHg, respectively. There was also a significant difference in PACD between the sexes. The mean values of BMI, WC and total muscle mass were significantly higher in men than in women, while the mean values of total fat mass and total and regional body fat percentages were significantly higher in women than in men. Men had higher percentages of current smoking, alcohol drinking, regular physical activity, hypertension and diabetes than women. There was no significant difference in the prevalence of moderate or heavy alcohol consumption, regular physical activity, diabetes and hypertension between study participants and excluded subjects (p = 0.053, 0.4, 0.437, 0.952, respectively) except in the prevalence of current smoking between the groups (p = 0.028).
Relationships Between Parameters of Obesity and IOP Figure 1 displays the positive, linear relationship between BMI and IOP in both sexes (r = 0.089, p50.001 in men, and r = 0.086, p50.001 in women). Table 2 shows the relationships between parameters of obesity and IOP by multivariate-adjusted linear regression. In men, IOP was positively associated with BMI, WC, total body fat mass, and total and regional body fat percentages in all adjusted models. In women, these parameters were positively associated with IOP in model 1 and model 2. However, only BMI, WC, total fat mass and trunk fat percentage displayed positive associations with IOP after adjusting for all
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TABLE 1 Baseline characteristics of study subjects.
N Age (years) Mean intraocular pressure (mmHg) Peripheral anterior chamber depth (PACD) in right eye (%) 1/4 Corneal thickness (CT) 1/4 CT5PACD 1/2 CT 41/2 CT Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%) Current smoker (%) Mild to moderate-drinker (%) Heavy-drinker (%) Regular physical exerciser (%) Hypertension (%) Diabetes (%) Post-menopause (%)
Men
Women
6600 43.5 ± 0.3 14.1 ± 0.1
8671 45.4 ± 0.3 13.8 ± 0.1
0.7 (0.2) 17.5 (1.4) 81.8 (1.4) 24.0 ± 0.1 83.9 ± 0.2 51.3 ± 0.1 15.6 ± 0.1 22.0 ± 0.1 24.2 ± 0.2 39.4 ± 0.3 47.6 (0.7) 68.3 (0.7) 18.7 (0.6) 28.1 (0.7) 31.3 (0.8) 8.7 (0.4)
1.3 (0.3) 20.3 (1.3) 78.4 (1.4) 23.2 ± 0.1 77.6 ± 0.2 35.8 ± 0.1 19.0 ± 0.1 32.9 ± 0.1 32.8 ± 0.2 68.7 ± 0.3 6.5 (0.4) 64.9 (0.7) 2.1 (0.2) 22.9 (0.7) 23.2 (0.6) 7.3 (0.4) 33.5 (0.8)
p* 50.001 50.001 50.001
50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001
Data are presented as means ± standard error (SE) or percentage (SE). *Obtained by Student’s t-test or chi-square test.
confounding variables. These associations were statistically significant but weak. Total body fat percentage and leg fat percentage were not significantly associated with IOP in women.
Prevalence of IOP 18 mmHg According to Parameters of Obesity Figure 2 shows the prevalence of IOP 18 mmHg according to quartiles of obesity parameters. The prevalence of IOP 18 mmHg in men increased significantly with increasing BMI, WC, total fat mass, and total and regional body fat percentages. Meanwhile, in women, the prevalence of IOP 18 mmHg increased with increasing BMI, WC and trunk fat percentage; however, neither total body fat mass nor total body fat percentage were significantly related to IOP 18 mmHg.
Risks of IOP 18 mmHg According to Parameters of Obesity Table 3 shows the ORs (95% CIs) for parameters of obesity in relation to having IOP 18 mmHg after adjusting for covariates. In the unadjusted model, men with higher BMI, WC, total body fat mass, and total and regional body fat percentages exhibited increasing trends in ORs for having IOP 18 mmHg; these relationships persisted after adjusting for all confounding factors in model 3 (p for trend 50.001 for BMI and total fat mass; p for trend = 0.038 for WC; 0.003 for total body fat percentage; 0.002 for trunk fat
percentage; 0.004 for leg fat percentage). Meanwhile, in women, the risk of having IOP 18 mmHg was significantly associated with BMI, WC and trunk fat percentage in the unadjusted model. After adjusting for all confounding factors (model 3), only BMI showed a significantly increasing trend in the risk of IOP 18 mmHg (p for trend = 0.004). However, the risk was not significantly changed according to any other parameters of obesity in women.
DISCUSSION In the present study, although showing statistically weak association, BMI, WC, total fat mass, and total and regional body fat percentages in men and BMI, WC, total fat mass and trunk fat percentage in women showed positive linear relationships with IOP. Various obesity parameters including BMI, WC, total body fat mass, and total and regional body fat percentages were associated with an increased risk of higher IOP (IOP 18 mmHg) in men after adjusting for confounding factors. However, we could not confirm this association in women. The results appear to have a considerable significance in respect of using nationally representative data and including adiposity as obesity parameter. Although several mechanisms have been proposed to explain the relationship between obesity and IOP, the pathophysiological mechanisms linking obesity to IOP remain unclear. Obesity is suggested to affect IOP by increasing intraorbital adipose tissue, blood viscosity, and episcleral venous pressure as well as impairing aqueous outflow facility.32 Moreover, Current Eye Research
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Intraocular Pressure and Parameters of Obesity
FIGURE 1 Bubble chart of correlation between IOP and BMI (Pearson’s correlation coefficient (r) = 0.089, p50.001 in men, and r = 0.086, p50.001 in women).
obesity-related systemic diseases, such as hypertension, diabetes, dyslipidemia and insulin resistance are reported to be associated with ocular hypertension. For example, elevated blood pressure may cause increased ultrafiltration of the aqueous humor via elevated ciliary artery pressure, and hyperglycemia may induce osmotic fluid shift into the intraocular space – both of these mechanisms can ultimately increase IOP.5,7,9,19,33,34 Elevated IOP tends to !
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aggravate the blood supply to the optic nerve head. Impaired vascular supply might be related to the instability of blood flow and perfusion to the eyes and subsequent endothelial dysfunction. Furthermore, obesity is known to cause vascular endothelial dysfunction.35,36 In addition, oxidative stress has been suggested to affect glaucomatous optic neuropathy via proteasome failure, human trabecular meshwork degeneration, and the consequent impairment of the
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TABLE 2 Multivariate-adjusted linear regression between obesity parameters and intraocular pressure.
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Men
Model 1 Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%) Model 2 Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%) Model 3 Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%)
Women
Beta
SE
p*
R-squared
SE
p*
R-squared
0.076 0.026 0.015 0.042 0.04 0.036 0.013
0.012 0.004 0.006 0.008 0.008 0.006 0.004
50.001 50.001 0.017 50.001 50.001 50.001 0.004
0.008 0.007 0.001 0.007 0.006 0.008 0.002
0.064 0.02 0.017 0.026 0.021 0.025 0.008
0.011 0.004 0.008 0.007 0.008 0.005 0.004
50.001 50.001 0.025 50.001 0.008 50.001 0.042
0.007 0.006 0.001 0.003 0.002 0.005 0.001
0.076 0.026 0.017 0.042 0.04 0.037 0.014
0.012 0.004 0.007 0.008 0.008 0.006 0.004
50.001 50.001 0.018 50.001 50.001 50.001 0.002
0.011 0.011 0.005 0.011 0.010 0.012 0.006
0.06 0.02 0.018 0.024 0.018 0.023 0.007
0.011 0.004 0.008 0.007 0.008 0.006 0.004
50.001 50.001 0.02 0.001 0.024 50.001 0.065
0.008 0.007 0.003 0.005 0.004 0.006 0.003
0.063 0.021 0.011 0.035 0.032 0.030 0.011
0.012 0.005 0.007 0.008 0.008 0.007 0.004
50.001 50.001 0.103 50.001 50.001 50.001 0.011
0.019 0.018 0.014 0.019 0.018 0.019 0.016
0.049 0.015 0.011 0.016 0.012 0.019 0.007
0.011 0.004 0.008 0.007 0.008 0.006 0.004
50.001 50.001 0.156 0.023 0.122 0.002 0.068
0.013 0.012 0.010 0.010 0.010 0.012 0.010
Beta
*Obtained by linear regression analyses. Model 1 is unadjusted. Model 2 is adjusted for age, alcohol consumption, smoking status and physical activity. Model 3 is adjusted for variables in model 2 plus hypertension and diabetes.
tissue ability to modulate outflow resistance.37 However, the causal mechanism of the impact of obesity on IOP remains unclear. Moreover, the impact of weight loss on IOP has never been investigated. Even though obesity appears to be positively associated with IOP, it is unclear whether statistically weak associations may explain clinical significance. Moreover, the results of published studies are inconsistent with respect to ethnicity, gender, age and measurements of obesity. Focusing on obesity measurements, a study in an adult European cohort reported that higher IOP was positively associated with BMI, WC and waist-to-hip ratio in univariate analyses. Waist-to-hip ratio remained significantly associated with higher IOP in multivariate analysis even after adjusting for central corneal thickness (CCT), and systemic conditions including diabetes, hypertension and dyslipidemia.15 Both cross-sectional and longitudinal studies from a large Japanese population reported that IOP was significantly associated with obesity measured by BMI in multivariate analyses.16 A subsequent cross-sectional, populationbased study in a southwestern island of Japan reported that higher BMI was significantly associated with higher IOP in multivariate analyses.17 Meanwhile, a cross-sectional study of Taiwanese adults reported that BMI was positively associated with IOP in young adults, especially men; however,
WC was not significantly associated with IOP in most groups and was positively associated with IOP only in elderly men from multivariate analyses.18 In two studies of South Koreans investigating the associations between IOP and metabolic syndrome and its components, abdominal obesity was not significantly associated with IOP among men or women in multiple regression analyses.38,39 However, a study about the relationship between IOP and systemic health parameters indicated that BMI was positively related to IOP in men but not in women.39 Some aspects of our study provide unique information relative to prior studies on obesity and IOP. In particular, the present study focused on body composition parameters, such as total and regional body fat. In addition to BMI and WC, total fat mass, total and regional fat percentages in men and total fat mass and trunk fat percentage in women were positively associated with IOP in linear regression analyses. In multivariate regression analyses after adjusting for confounding factors, men with higher BMI, total body fat mass, and total and regional body fat percentages exhibited increasing trends in ORs for having high IOP. However, the trends regarding the risk of high IOP were not significantly associated with any body composition parameters in women. Adiposity is potentially pathological to the cardiovascular system via excessive fat mass mechanisms and/or adipocyte Current Eye Research
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Intraocular Pressure and Parameters of Obesity
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FIGURE 2 The prevalence of higher intraocular pressure (18 mmHg) according to quartile groups of obesity parameters. *p for trend 50.05.
and adipose tissue dysfunction. Proinflammatory adipokines secreted from fat mass promote insulin resistance and induce a direct catabolic effect on muscle. This vicious circle leads to further fat accumulation and metabolic derangement.40,41 Several previous epidemiological studies indicate that elevated body fat percentage is associated with increased risks of developing cardiometabolic dysregulation, metabolic syndrome and cardiovascular risk factors, which are associated with high IOP even in subjects with a normal BMI.42–44 Thus it might be important to assess adiposity as a measure of obesity in order to identify subjects with an increased risk of high IOP and cardiometabolic risk. Another noteworthy finding of the present study is sex disparities in the relationships between IOP and measurements of obesity. Sex differences in regional fat distribution might explain this finding. Men have more visceral fat, whereas women have more subcutaneous fat. Adipose tissue distributed in the abdominal viscera confers a greater cardiovascular risk than subcutaneous adipose tissue. The greater visceral fat distribution in men might affect IOP in the same manner as cardiovascular disorders that are known to be associated with high IOP.45 In addition, estrogen is suggested to play a major role in sex !
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differences. Besides influencing the mediation of the fat distribution, estrogen may exert an ocular hypotensive effect by influencing the aqueous production and outflow systems. Estrogen regulates smooth muscle tone and vascular resistance by augmenting the activity of endothelial-based nitric oxide synthase.46 These mechanisms may be the cause of the increased susceptibility of the effects of obesity on high IOP in men. However, the precise underlying mechanisms are not well understood. The present study has some limitations. First, the cross-sectional design preludes the deduction of cause-and-effect relationships between parameters of obesity and increased IOP. Second, we were not able to assess the degree of visceral adiposity or serum sex hormone levels of the subjects because of the limited data from the KNHANES. Third, we could not include CCT in the analysis by a lack of data in the KNHANES. CCT is one of the risk factors for openangle glaucoma and this may have impact on the measurement of IOP.47,48 The relationship between obesity parameters and IOP might have been more accurate if the CCT was adjusted as a confounding factor. Fourth, the use of DEXA scan leads to hydration-induced errors because lean tissue hydration varies systematically with fluid distribution.49
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TABLE 3 Odds ratios (95% confidence intervals) of having intraocular pressure 18 mmHg according to parameters of obesity. Men
Women
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
1 1.35 (1.03–1.76) 1.75 (1.41–2.18) 50.001
1 1.35 (1.04–1.76) 1.75 (1.41–2.18) 50.001
1 1.31 (1.00–1.71) 1.64 (1.31–2.05) 50.001
1 1.56 (1.23–1.98) 1.61 (1.27–2.03) 50.001
1 1.56 (1.22–1.99) 1.59 (1.23–2.05) 50.001
1 1.52 (1.19–1.94) 1.40 (1.09–1.79) 0.004
1 1.4 (1.14–1.71) 0.001
1 1.38 (1.12–1.69) 0.002
1 1.25 (1.01–1.55) 0.038
1 1.39 (1.13–1.72) 0.002
1 1.36 (1.08–1.71) 0.009
1 1.23 (0.98–1.54) 0.079
1 1.07 (0.79–1.46) 0.89 (0.66–1.21) 1.31 (0.97–1.76) 0.134
1 1.11 (0.81–1.52) 0.95 (0.69–1.31) 1.42 (1.03–1.94) 0.054
1 1.08 (0.79–1.48) 0.90 (0.65–1.23) 1.30 (0.95–1.79) 0.166
1 0.97 (0.74–1.28) 1.04 (0.80–1.36) 1.22 (0.93–1.60) 0.114
1 1.00 (0.76–1.32) 1.08 (0.83–1.40) 1.28 (0.97–1.68) 0.063
1 0.97 (0.73–1.30) 1.04 (0.80–1.35) 1.15 (0.88–1.51) 0.264
1 1.35 (1.02–1.78) 1.56 (1.19–2.04) 1.95 (1.50–2.54) 50.001
1 1.30 (0.98–1.72) 1.52 (1.16–2.00) 1.97 (1.51–2.57) 50.001
1 1.30 (0.98–1.73) 1.44 (1.09–1.89) 1.79 (1.36–2.36) 50.001
1 1.05 (0.79–1.4) 1.03 (0.77–1.37) 1.32 (0.98–1.77) 0.081
1 1.02 (0.77–1.36) 0.99 (0.74–1.32) 1.28 (0.95–1.73) 0.123
1 1.01 (0.76–1.35) 0.93 (0.69–1.25) 1.15 (0.85–1.55) 0.455
1 1.09 (0.83–1.43) 1.59 (1.22–2.07) 1.57 (1.18–2.09) 50.001
1 1.05 (0.80–1.39) 1.55 (1.19–2.02) 1.58 (1.18–2.10) 50.001
1 1.02 (0.77–1.34) 1.44 (1.11–1.88) 1.43 (1.06–1.91) 0.003
1 1.19 (0.90–1.57) 1.12 (0.83–1.50) 1.16 (0.85–1.60) 0.438
1 1.15 (0.87–1.52) 1.07 (0.79–1.45) 1.10 (0.8–1.52) 0.685
1 1.10 (0.83–1.47) 0.97 (0.71–1.32) 0.99 (0.72–1.37) 0.752
1 0.99 (0.74–1.33) 1.49 (1.15–1.93) 1.60 (1.23–2.09) 50.001
1 0.94 (0.70–1.26) 1.45 (1.12–1.88) 1.57 (1.19–2.05) 50.001
1 0.92 (0.68–1.23) 1.38 (1.05–1.79) 1.40 (1.05–1.86) 0.002
1 1.26 (0.94–1.68) 1.30 (0.97–1.73) 1.41 (1.03–1.92) 0.031
1 1.21 (0.90–1.63) 1.23 (0.91–1.67) 1.32 (0.94–1.86) 0.114
1 1.17 (0.86–1.59) 1.13 (0.83–1.53) 1.17 (0.83–1.65) 0.45
1 1.43 (1.07–1.9) 1.40 (1.05–1.86) 1.67 (1.24–2.26) 0.002
1 1.43 (1.07–1.90) 1.40 (1.05–1.86) 1.73 (1.28–2.34) 0.001
1 1.38 (1.03–1.86) 1.34 (1.01–1.79) 1.63 (1.20–2.22) 0.004
1 0.93 (0.72–1.21) 0.92 (0.71–1.20) 0.80 (0.60–1.08) 0.155
1 0.95 (0.73–1.24) 0.94 (0.72–1.23) 0.82 (0.61–1.10) 0.191
1 0.92 (0.71–1.21) 0.95 (0.72–1.24) 0.81 (0.60–1.10) 0.211
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Body mass index (kg/m ) 523 23–25 25 p for trend Waist circumference (cm) Men 590 or Women 580 Men 90 or Women 80 p for trend Total muscle mass Q1 Q2 Q3 Q4 p for trend Total fat mass Q1 Q2 Q3 Q4 p for trend Total fat percentage Q1 Q2 Q3 Q4 p for trend Trunk fat percentage Q1 Q2 Q3 Q4 p for trend Leg fat percentage Q1 Q2 Q3 Q4 p for trend
Model 1 is unadjusted. Model 2 is adjusted for age, alcohol consumption, smoking status and physical activity. Model 3 is adjusted for variables in model 2 plus hypertension and diabetes.
Particularly in conditions with altered fluid balance, such as cardiac and renal failure, it may vary in accuracy.50 However, individuals with heart failure were not considered in the analyses. Fifth, the ophthalmic information, such as CCT and gonioscopy data lacked in this study and IOP only lends one piece of information to the diagnosis of glaucoma, therefore the clinical relevance of our study findings to developing glaucoma remains to be determined from further studies. Lastly, we did not perform multiple comparisons because the objective of our study is to find associated factors using exploratory analyses based on nationally representative data. The exploratory analyses are regarded as a reasonable way to enhance detection of general trends.
Despite these limitations, this study has several strengths. As nationally representative data, the KNHANES enables us to evaluate the accurate health status of contemporary Koreans as a single ethnicity. Additionally, the study also assessed adiposity as parameter of obesity. In conclusion, IOP is positively associated with BMI, WC, total fat mass, and total and regional body fat percentages in men, and with BMI, WC, total fat mass, and trunk fat percentage in women. Increased BMI, WC, and total and regional body fat are positively associated with a risk of higher IOP (IOP 18 mmHg) especially in Korean men. Screening for obesity including adiposity may be helpful for identification of individuals at risk for high IOP. Current Eye Research
Intraocular Pressure and Parameters of Obesity
ACKNOWLEDGEMENTS We would like to greatly appreciate the participants in the 2008–2010 Korea National Health and Nutrition Examination Survey.
DECLARATION OF INTEREST The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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REFERENCES 1. Vohra R, Tsai JC, Kolko M. The role of inflammation in the pathogenesis of glaucoma. Survey Ophthalmol 2013;58: 311–320. 2. Resnikoff S, Pascolini D, Etya’ale D, Kocur I, Pararajasegaram R, Pokharel GP, et al. Global data on visual impairment in the year 2002. Bull World Health Org 2004;82:844–851. 3. Sommer A, Tielsch JM, Katz J, Quigley HA, Gottsch JD, Javitt J, et al. Relationship between intraocular pressure and primary open angle glaucoma among white and black Americans. The Baltimore Eye Survey. Arch Ophthalmol 1991;109:1090–1095. 4. Leske MC, Connell AM, Wu SY, Hyman LG, Schachat AP. Risk factors for open-angle glaucoma. The Barbados Eye Study. Arch Ophthalmol 1995;113:918–924. 5. Goldberg I. Relationship between intraocular pressure and preservation of visual field in glaucoma. Sur Ophthalmol 2003;48:S3–S7. 6. Kass MA, Heuer DK, Higginbotham EJ, Johnson CA, Keltner JL, Miller JP, et al. The Ocular Hypertension Treatment Study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. Arch Ophthalmol 2002;120:701–713; discussion 829–830. 7. Dielemans I, Vingerling JR, Algra D, Hofman A, Grobbee DE, de Jong PT. Primary open-angle glaucoma, intraocular pressure, and systemic blood pressure in the general elderly population. The Rotterdam Study. Ophthalmology 1995;102:54–60. 8. Hennis A, Wu SY, Nemesure B, Leske MC. Hypertension, diabetes, and longitudinal changes in intraocular pressure. Ophthalmology 2003;110:908–914. 9. Mitchell P, Smith W, Chey T, Healey PR. Open-angle glaucoma and diabetes: the Blue Mountains eye study, Australia. Ophthalmology 1997;104:712–718. 10. Khang YH, Yun SC. Trends in general and abdominal obesity among Korean adults: findings from 1998, 2001, 2005, and 2007 Korea National Health and Nutrition Examination Surveys. J Korean Med Sci 2010;25:1582–1588. 11. Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B, et al. Obesity and severe obesity forecasts through 2030. Am J Prev Med 2012;42:563–570. 12. Kannel WB, Cupples LA, Ramaswami R, Stokes 3rd J, Kreger BE, Higgins M. Regional obesity and risk of cardiovascular disease: the Framingham Study. J Clin Epidemiol 1991;44:183–190. 13. Rutkowski P, Klassen A, Sebekova K, Bahner U, Heidland A. Renal disease in obesity: the need for greater attention. J Renal Nutr 2006;16:216–223. !
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14. Rapp K, Schroeder J, Klenk J, Stoehr S, Ulmer H, Concin H, et al. Obesity and incidence of cancer: a large cohort study of over 145,000 adults in Austria. Br J Cancer 2005;93: 1062–1067. 15. Hoehn R, Mirshahi A, Hoffmann EM, Kottler UB, Wild PS, Laubert-Reh D, et al. Distribution of intraocular pressure and its association with ocular features and cardiovascular risk factors: the Gutenberg health study. Ophthalmology 2013;120:961–968. 16. Mori K, Ando F, Nomura H, Sato Y, Shimokata H. Relationship between intraocular pressure and obesity in Japan. Int J Epidemiol 2000;29:661–666. 17. Tomoyose E, Higa A, Sakai H, Sawaguchi S, Iwase A, Tomidokoro A, et al. Intraocular pressure and related systemic and ocular biometric factors in a populationbased study in Japan: the Kumejima study. Am J Ophthalmol 2010;150:279–286. 18. Lin CP, Lin YS, Wu SC, Ko YS. Age- and gender-specific association between intraocular pressure and metabolic variables in a Taiwanese population. Eur J Int Med 2012;23: 76–82. 19. Oh SW, Lee S, Park C, Kim DJ. Elevated intraocular pressure is associated with insulin resistance and metabolic syndrome. Diabetes Metabolism Res Rev 2005;21: 434–440. 20. Joseph L, Wasir JS, Misra A, Vikram NK, Goel K, Pandey RM, et al. Appropriate values of adiposity and lean body mass indices to detect cardiovascular risk factors in Asian Indians. Diabetes Technol Ther 2011;13:899–906. 21. Prior BM, Cureton KJ, Modlesky CM, Evans EM, Sloniger MA, Saunders M, et al. In vivo validation of whole body composition estimates from dual-energy X-ray absorptiometry. J Appl Physiol 1997;83:623–630. 22. Agarwal DP. Cardioprotective effects of light-moderate consumption of alcohol: a review of putative mechanisms. Alcohol Alcohol 2002;37:409–415. 23. Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr 2006;9: 755–762. 24. Weisell RC. Body mass index as an indicator of obesity. Asia Pac J Clin Nutr 2002;11:S681–S684. 25. Oh SW, Shin SA, Yun YH, Yoo T, Huh BY. Cut-off point of BMI and obesity-related comorbidities and mortality in middle-aged Koreans. Obes Res 2004;12: 2031–2040. 26. Lee S, Park HS, Kim SM, Kwon HS, Kim DY, Kim DJ, et al. Cut-off points of waist circumference for defining abdominal obesity in the Korean population. Korean J Obes 2006; 15:1–9. 27. Lee K. Body composition and vitamin D status: the Korea National Health And Nutritional Examination Survey IV (KNHANES IV). J Hum Nutr Diet 2013;26:105–113. 28. The Korea National Health and Nutrition Examination Survey (KNHANES) 2008–2010, Korea Centers for Disease Control and Prevention. Available from: http://knhanes.cdc.go.kr/knhanes [20 May 2014]. 29. The Advanced Glaucoma Intervention Study (AGIS): 7. The relationship between control of intraocular pressure and visual field deterioration. The AGIS Investigators. Am J Ophthalmol 2000;130:429–440. 30. Kweon S, Kim Y, Jang MJ, Kim K, Choi S, Chun C, et al. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol 2014;43:69–77. 31. Yoon KC, Mun GH, Kim SD, Kim SH, Kim CY, Park KH, et al. Prevalence of eye diseases in South Korea: Data from the Korea National Health and Nutritional Examination Survey 2008-2009. Korean J Ophthalmol 2011;25:421–433.
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10 H.-D. Jang et al. 32. Halpern DL, Grosskreutz CL. Glaucomatous optic neuropathy: mechanisms of disease. Ophthalmol Clin North Am 2002;15:61–68. 33. Bonomi L, Marchini G, Marraffa M, Bernardi P, Morbio R, Varotto A. Vascular risk factors for primary open angle glaucoma: the Egna-Neumarkt Study. Ophthalmology 2000;107:1287–1293. 34. Cheung N, Wong TY. Obesity and eye diseases. Surv Ophthalmol 2007;52:180–195. 35. Grieshaber MC, Flammer J. Blood flow in glaucoma. Curr Opin Ophthalmol 2005;16:79–83. 36. Yudkin JS, Eringa E, Stehouwer CD. ‘‘Vasocrine’’ signalling from perivascular fat: a mechanism linking insulin resistance to vascular disease. Lancet 2005;365:1817–1820. 37. Caballero M, Liton PB, Epstein DL, Gonzalez P. Proteasome inhibition by chronic oxidative stress in human trabecular meshwork cells. Biochem Biophys Res Comm 2003;308:346–352. 38. Park BJ, Park JO, Kang HT, Lee YJ. Elevated intraocular pressure is associated with metabolic syndrome in postmenopausal women: the Korean National Health and Nutrition Examination Survey. Menopause 2013;20: 742–746. 39. Park SS, Lee EH, Jargal G, Paek D, Cho SI. The distribution of intraocular pressure and its association with metabolic syndrome in a community. J Prev Med Public Health 2010; 43:125–130. 40. Bays HE, Gonzalez-Campoy JM, Bray GA, Kitabchi AE, Bergman DA, Schorr AB, et al. Pathogenic potential of adipose tissue and metabolic consequences of adipocyte hypertrophy and increased visceral adiposity. Expert Rev Cardiovasc Ther 2008;6:343–368. 41. Bays HE. Adiposopathy is ‘‘sick fat’’ a cardiovascular disease? J Am Coll Cardiol 2011;57:2461–2473.
42. Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S, Korinek J, et al. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J 2010;31:737–746. 43. Shea JL, King MT, Yi Y, Gulliver W, Sun G. Body fat percentage is associated with cardiometabolic dysregulation in BMI-defined normal weight subjects. Nutr Metab Cardiovasc Dis 2012;22:741–747. 44. Gomez-Ambrosi J, Silva C, Galofre JC, Escalada J, Santos S, Gil MJ, et al. Body adiposity and type 2 diabetes: increased risk with a high body fat percentage even having a normal BMI. Obesity 2011;19:1439–1444. 45. Nedungadi TP, Clegg DJ. Sexual dimorphism in body fat distribution and risk for cardiovascular diseases. J Cardiovasc Transl Res 2009;2:321–327. 46. Vajaranant TS, Pasquale LR. Estrogen deficiency accelerates aging of the optic nerve. Menopause 2012;19:942–947. 47. Hwang YH, Kim HK, Sohn YH; Namil Study Group, Korean Glaucoma Society. Central corneal thickness in a Korean population: the Namil Study. Invest Ophthalmol Vis Sci 2012;53:6851–6855. 48. Su DH, Wong TY, Foster PJ, Tay WT, Saw SM, Aung T. Central corneal thickness and its associations with ocular and systemic factors: the Singapore Malay Eye Study. Am J Ophthalmol 2009;147:709–716. 49. St-Onge MP, Wang Z, Horlick M, Wang J, Heymsfield SB. Dual-energy X-ray absorptiometry lean soft tissue hydration: independent contributions of intra- and extracellular water. Am J Physiol Endocrinol Metab 2004;287:E842–E847. 50. Lang PO, Trivalle C, Vogel T, Proust J, Papazian JP. Markers of metabolic and cardiovascular health in adults: comparative analysis of DEXA-based body composition components and BMI categories. J Cardiol 2014 Apr 29. doi: 10.1016/j.jjcc.2014.03.010. [Epub ahead of print].
Current Eye Research
RESEARCH REPORT
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Relationship Between Intraocular Pressure and Parameters of Obesity in Korean Adults: The 2008–2010 Korea National Health and Nutrition Examination Survey Hyung-Deok Jang1*, Do Hoon Kim1*, Kyungdo Han2, Suk Gyu Ha3, Yang Hyun Kim1, Jae Woo Kim1, Ji Young Park1, Su Jung Yoon1, Dong Wook Jung1, Sang Woon Park1 and Ga Eun Nam1 1
Department of Family Medicine, Korea University College of Medicine, Seoul, South Korea, 2Department of Biostatistics, Catholic University College of Medicine, Seoul, South Korea, and 3Department of Ophthalmology, Korea University College of Medicine, Seoul, South Korea
ABSTRACT Purpose: To examine the associations of various parameters of obesity including adiposity with intraocular pressure (IOP) using nationally representative data of South Korean adults. Material and methods: This cross-sectional study analyzed the data from the 2008–2010 Korea National Health and Nutrition Examination Survey. A total of 15,271 subjects (6600 men and 8671 women) participated. Body mass index (BMI), waist circumference (WC), total body fat mass, and total and regional body fat percentage were measured as parameters of obesity. Results: IOP showed positive linear associations with BMI, WC, total fat mass, and total and regional body fat percentages in men, and with BMI, WC, total fat mass, and trunk fat percentage in women after adjusting for confounding variables. Men with higher BMI, WC, total fat mass, and total and regional body fat percentages exhibited increasing trends in odd ratios for having IOP 18 mmHg after adjusting for all confounding factors (p for trend 50.001 for BMI and total fat mass; p for trend = 0.038 for WC; 0.003 for total body fat percentage; 0.002 for trunk fat percentage; 0.004 for leg fat percentage). However, only BMI showed a significantly increasing trend in the risk of IOP 18 mmHg in women. Conclusions: In addition to BMI, WC and total fat mass, total and regional body fat percentage in men and trunk fat percentage in women are positively associated with IOP. Increased BMI, WC, and total and regional body fat are positively associated with a risk of higher IOP (IOP 18 mmHg), especially in Korean men. Keywords: Adiposity, body mass index, intraocular pressure, obesity, waist circumference
acquired blindness.1,2 Many studies aiming to identify related risk factors have been performed. Elevated intraocular pressure (IOP) is a major risk factor for glaucoma that can be treated.3–5 Moreover, reducing IOP lowers the risk of glaucoma.6 Therefore, early
INTRODUCTION Glaucoma is an ocular disorder featured by the ongoing loss of retinal ganglion cells and their axons; it is one of the most common causes of
Received 7 March 2014; revised 24 September 2014; accepted 5 October 2014; published online 6 November 2014 *These authors contributed equally to this work. Correspondence: Ga Eun Nam, MD, MSc, Department of Family Medicine, Korea University Ansan Hospital, Korea University College of Medicine, 516 Gojan-dong, Danwon-gu, Ansan-si, Gyeonggi-do, 425-707, South Korea. Tel: 82-31-412-5360. Fax: 82-31-412-5364. E-mail: [email protected]
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detection and modification of risk factors for elevated IOP may be important for preventing glaucoma in a healthcare perspective. From several evidences, elevated IOP appears to be associated with cardiovascular risk factors, such as obesity and components of metabolic syndrome including abdominal obesity, high blood pressure, hyperglycemia, and abnormal levels of serum lipid profiles.7–9 The prevalence of obesity has increased rapidly worldwide including South Korea. Although the increasing prevalence of obesity has stabilized over the past decade, obesity-related diseases including diabetes, hypertension, dyslipidemia, chronic kidney disease, cardiovascular disease, and cancer remain prevalent and lead to great burdens on society.10–14 Several studies have investigated the relationship between obesity and IOP, and showed inconsistent results with respect to age, sex, ethnicity and measurement of obesity.15–19 Moreover, most of these studies used only body mass index (BMI) or waist circumference (WC) as measurements of obesity. Although these measurements are the most popular tools for evaluating obesity because of their simplicity and low cost, they might not precisely correspond to the actual degree of adiposity. Thus, direct assessment of adiposity might be a better index of obesity and more valuable tool for understanding the relationship between adiposity and metabolic changes because it is better associated with metabolic and cardiovascular risk factors.20 Dual-energy X-ray absorptiometry (DEXA) accurately detects adiposity and provides information about the total and regional percentages of fat.21 However, no study has explored directly the association between increased IOP and adiposity. Therefore, we investigated the associations of various parameters of obesity, including total body fat mass and total and regional body fat percentages, with IOP using nationally representative data of South Korean adults.
the basis of household registries using a stratified, multistage and probability-based sampling design with proportional allocation. A total of 17,446 subjects aged 19 years or older underwent eye examinations in the 2008–2010 KNHANES. We excluded subjects with any missing data from analyses. Subjects who had suffered from any cancer, chronic hepatitis or cirrhosis; those with a history of chronic kidney disease; or those with a glomerular filtration rate less than 30 mL/min/1.73 m2 were also excluded. We also excluded subjects who had been diagnosed with glaucoma from physician based on the self-reported questionnaire. Finally, 15,271 subjects (6600 men and 8671 women) were included in the analyses. All participants in the survey signed an informed consent form prior to participation. This survey was reviewed and approved by the Institutional Review Board of the KCDC.
Lifestyle Variables All subjects were asked about their lifestyle characteristics including alcohol consumption, smoking status and physical activity. Based on their average alcohol intake per day in the month before the interview, subjects were categorized as non-drinkers, light to moderate drinkers (1–30 g/day) or heavy drinkers (430 g/day).22 Subjects were subdivided into non-smokers, ex-smokers and current smokers according to their answers on the self-reported questionnaire. On the basis of their responses to the International Physical Activity Questionnaire, subjects were considered regular physical exercisers if they performed moderate exercise more than five times per week for over 30 min per session or performed vigorous exercise more than three times per week for over 20 min per session.23
Anthropometric Measurements and Body Composition Analyses MATERIALS AND METHODS Survey Overview and Study Subjects The data obtained from the 2008–2010 Korea National Health and Nutrition Examination Survey (KNHANES) were analyzed in this study. The KNHANES is designed to accurately assess the health and nutritional status of the non-institutionalized civilian population of Korea and has been conducted annually since 1998 by the Division of Chronic Disease Surveillance, Korea Centers for Disease Control and Prevention (KCDC). It comprises three surveys: health interview, health examination and nutrition examination surveys. Participants are selected from sampling household units defined on
Specially trained examiners performed anthropometric measurements of the subjects. Body weight and height were measured with the subjects being barefoot and wearing light clothing and BMI was calculated using the formula: body weight (kg)/ height2 (m2). WC was measured at the midpoint between the lower border of the rib cage and the iliac crest while subjects were standing. Obesity was defined as a BMI 25 kg/m2 and overweight as 23 BMI525 kg/m2.24,25 The cutoff points for abdominal obesity were defined as WC 90 cm for men and WC 85 cm for women.26 Total and regional (i.e. trunk and leg) body fat mass and lean mass were measured using whole-body DEXA (QDR 4500A fan-beam densitometer, Hologic Current Eye Research
Intraocular Pressure and Parameters of Obesity Inc., Bedford, MA) by qualified technicians according to manufacturer’s acquisition procedures. The results of the DEXA were analyzed using industry standard techniques at the Korean Society of Osteoporosis with Hologic Discovery Software (version 13.1, Hologic Inc., Bedford, MA). Total body fat percentage was calculated as follows: total body fat mass/total body mass (fat mass + lean mass + total body mineral content) 100. Trunk fat percentage was measured as follows: (trunk fat mass/trunk mass) 100. Leg fat percentage was measured as follows: (leg fat mass/ leg mass) 100.27,28
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relationships between parameters of obesity and IOP. After the subjects were divided into quartiles for each parameters of obesity, the differences in the prevalence of high IOP with respect to the quartiles of obesity parameters were examined. Odds ratios (ORs) and 95% confidence intervals (CIs) for having IOP 18 mmHg according to obesity parameters were obtained by hierarchical multivariate logistic regression analyses. Univariate analyses were performed in model 1. Model 2 was adjusted for age, alcohol consumption, smoking status and physical activity. Finally, model 3 was adjusted for all variables in model 2 plus hypertension and diabetes.
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RESULTS Complete ophthalmologic examinations were performed by study ophthalmologists. A visual acuity was measured using an international standard vision chart (Jin’s Vision Chart; Seoul, Korea) at a distance of 4 m. Refractive errors were measured using an autorefractor-keratometer (KR8800; Topcon, Tokyo, Japan). A slit lamp examination including assessment of peripheral anterior chamber depth by the Van Herick method (Haag-Streit model BQ-900; HaagStreit AG, Koeniz, Switzerland) was performed. Fundus photographs were taken with a digital nonmydriatic fundus camera (TRC-NW6S; Topcon, Tokyo, Japan, and Nikon D-80 digital camera; Nikon, Tokyo, Japan). IOP was measured with a Goldmann applanation tonometer (GAT; Haag-Streit; Haag-Streit AG, Koeniz, Switzerland) once for each eye from right to left. Mean values from both eyes were used in the analyses because IOP in the right and left eyes was highly correlated (Pearson correlation coefficient (r) = 0.79, p50.001). We categorized as IOP 518 mmHg or IOP 18 mmHg.29
Statistical Analysis This survey was technically supported by 30 expert committees and quality control of the survey was verified based on the consensus of committee members. All the variable data were entered into a password-protected Microsoft Office Access database and imaging data were retrieved directly from the imaging equipment and stored in their respective computers.30,31 SAS version 9.2 for Windows (SAS institute, Cary, NC) was used for all statistical analyses. Two-sided p values 50.05 were considered statistically significant. Data are presented as mean ± standard error (SE) or as percentage (SE). Student’s t-test and Chi-square test for continuous and categorical variables, respectively, were used to compare the means of baseline characteristics between sexes. Multivariate adjusted linear regression analyses were used to examine the !
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Baseline Characteristics The baseline characteristics of study subjects are presented in Table 1. The mean age was 44.4 ± 0.3 years and the proportion of male subjects was 43.2%. The mean IOP level for the whole sample was 14.0 ± 0.1 mmHg. All baseline measurements were significantly different by gender. The mean IOP in men and women was 14.1 ± 0.1 and 13.8 ± 0.1 mmHg, respectively. There was also a significant difference in PACD between the sexes. The mean values of BMI, WC and total muscle mass were significantly higher in men than in women, while the mean values of total fat mass and total and regional body fat percentages were significantly higher in women than in men. Men had higher percentages of current smoking, alcohol drinking, regular physical activity, hypertension and diabetes than women. There was no significant difference in the prevalence of moderate or heavy alcohol consumption, regular physical activity, diabetes and hypertension between study participants and excluded subjects (p = 0.053, 0.4, 0.437, 0.952, respectively) except in the prevalence of current smoking between the groups (p = 0.028).
Relationships Between Parameters of Obesity and IOP Figure 1 displays the positive, linear relationship between BMI and IOP in both sexes (r = 0.089, p50.001 in men, and r = 0.086, p50.001 in women). Table 2 shows the relationships between parameters of obesity and IOP by multivariate-adjusted linear regression. In men, IOP was positively associated with BMI, WC, total body fat mass, and total and regional body fat percentages in all adjusted models. In women, these parameters were positively associated with IOP in model 1 and model 2. However, only BMI, WC, total fat mass and trunk fat percentage displayed positive associations with IOP after adjusting for all
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TABLE 1 Baseline characteristics of study subjects.
N Age (years) Mean intraocular pressure (mmHg) Peripheral anterior chamber depth (PACD) in right eye (%) 1/4 Corneal thickness (CT) 1/4 CT5PACD 1/2 CT 41/2 CT Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%) Current smoker (%) Mild to moderate-drinker (%) Heavy-drinker (%) Regular physical exerciser (%) Hypertension (%) Diabetes (%) Post-menopause (%)
Men
Women
6600 43.5 ± 0.3 14.1 ± 0.1
8671 45.4 ± 0.3 13.8 ± 0.1
0.7 (0.2) 17.5 (1.4) 81.8 (1.4) 24.0 ± 0.1 83.9 ± 0.2 51.3 ± 0.1 15.6 ± 0.1 22.0 ± 0.1 24.2 ± 0.2 39.4 ± 0.3 47.6 (0.7) 68.3 (0.7) 18.7 (0.6) 28.1 (0.7) 31.3 (0.8) 8.7 (0.4)
1.3 (0.3) 20.3 (1.3) 78.4 (1.4) 23.2 ± 0.1 77.6 ± 0.2 35.8 ± 0.1 19.0 ± 0.1 32.9 ± 0.1 32.8 ± 0.2 68.7 ± 0.3 6.5 (0.4) 64.9 (0.7) 2.1 (0.2) 22.9 (0.7) 23.2 (0.6) 7.3 (0.4) 33.5 (0.8)
p* 50.001 50.001 50.001
50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001 50.001
Data are presented as means ± standard error (SE) or percentage (SE). *Obtained by Student’s t-test or chi-square test.
confounding variables. These associations were statistically significant but weak. Total body fat percentage and leg fat percentage were not significantly associated with IOP in women.
Prevalence of IOP 18 mmHg According to Parameters of Obesity Figure 2 shows the prevalence of IOP 18 mmHg according to quartiles of obesity parameters. The prevalence of IOP 18 mmHg in men increased significantly with increasing BMI, WC, total fat mass, and total and regional body fat percentages. Meanwhile, in women, the prevalence of IOP 18 mmHg increased with increasing BMI, WC and trunk fat percentage; however, neither total body fat mass nor total body fat percentage were significantly related to IOP 18 mmHg.
Risks of IOP 18 mmHg According to Parameters of Obesity Table 3 shows the ORs (95% CIs) for parameters of obesity in relation to having IOP 18 mmHg after adjusting for covariates. In the unadjusted model, men with higher BMI, WC, total body fat mass, and total and regional body fat percentages exhibited increasing trends in ORs for having IOP 18 mmHg; these relationships persisted after adjusting for all confounding factors in model 3 (p for trend 50.001 for BMI and total fat mass; p for trend = 0.038 for WC; 0.003 for total body fat percentage; 0.002 for trunk fat
percentage; 0.004 for leg fat percentage). Meanwhile, in women, the risk of having IOP 18 mmHg was significantly associated with BMI, WC and trunk fat percentage in the unadjusted model. After adjusting for all confounding factors (model 3), only BMI showed a significantly increasing trend in the risk of IOP 18 mmHg (p for trend = 0.004). However, the risk was not significantly changed according to any other parameters of obesity in women.
DISCUSSION In the present study, although showing statistically weak association, BMI, WC, total fat mass, and total and regional body fat percentages in men and BMI, WC, total fat mass and trunk fat percentage in women showed positive linear relationships with IOP. Various obesity parameters including BMI, WC, total body fat mass, and total and regional body fat percentages were associated with an increased risk of higher IOP (IOP 18 mmHg) in men after adjusting for confounding factors. However, we could not confirm this association in women. The results appear to have a considerable significance in respect of using nationally representative data and including adiposity as obesity parameter. Although several mechanisms have been proposed to explain the relationship between obesity and IOP, the pathophysiological mechanisms linking obesity to IOP remain unclear. Obesity is suggested to affect IOP by increasing intraorbital adipose tissue, blood viscosity, and episcleral venous pressure as well as impairing aqueous outflow facility.32 Moreover, Current Eye Research
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Intraocular Pressure and Parameters of Obesity
FIGURE 1 Bubble chart of correlation between IOP and BMI (Pearson’s correlation coefficient (r) = 0.089, p50.001 in men, and r = 0.086, p50.001 in women).
obesity-related systemic diseases, such as hypertension, diabetes, dyslipidemia and insulin resistance are reported to be associated with ocular hypertension. For example, elevated blood pressure may cause increased ultrafiltration of the aqueous humor via elevated ciliary artery pressure, and hyperglycemia may induce osmotic fluid shift into the intraocular space – both of these mechanisms can ultimately increase IOP.5,7,9,19,33,34 Elevated IOP tends to !
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aggravate the blood supply to the optic nerve head. Impaired vascular supply might be related to the instability of blood flow and perfusion to the eyes and subsequent endothelial dysfunction. Furthermore, obesity is known to cause vascular endothelial dysfunction.35,36 In addition, oxidative stress has been suggested to affect glaucomatous optic neuropathy via proteasome failure, human trabecular meshwork degeneration, and the consequent impairment of the
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TABLE 2 Multivariate-adjusted linear regression between obesity parameters and intraocular pressure.
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Men
Model 1 Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%) Model 2 Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%) Model 3 Body mass index (kg/m2) Waist circumference (cm) Total muscle mass (kg) Total fat mass (kg) Total fat percentage (%) Trunk fat percentage (%) Leg fat percentage (%)
Women
Beta
SE
p*
R-squared
SE
p*
R-squared
0.076 0.026 0.015 0.042 0.04 0.036 0.013
0.012 0.004 0.006 0.008 0.008 0.006 0.004
50.001 50.001 0.017 50.001 50.001 50.001 0.004
0.008 0.007 0.001 0.007 0.006 0.008 0.002
0.064 0.02 0.017 0.026 0.021 0.025 0.008
0.011 0.004 0.008 0.007 0.008 0.005 0.004
50.001 50.001 0.025 50.001 0.008 50.001 0.042
0.007 0.006 0.001 0.003 0.002 0.005 0.001
0.076 0.026 0.017 0.042 0.04 0.037 0.014
0.012 0.004 0.007 0.008 0.008 0.006 0.004
50.001 50.001 0.018 50.001 50.001 50.001 0.002
0.011 0.011 0.005 0.011 0.010 0.012 0.006
0.06 0.02 0.018 0.024 0.018 0.023 0.007
0.011 0.004 0.008 0.007 0.008 0.006 0.004
50.001 50.001 0.02 0.001 0.024 50.001 0.065
0.008 0.007 0.003 0.005 0.004 0.006 0.003
0.063 0.021 0.011 0.035 0.032 0.030 0.011
0.012 0.005 0.007 0.008 0.008 0.007 0.004
50.001 50.001 0.103 50.001 50.001 50.001 0.011
0.019 0.018 0.014 0.019 0.018 0.019 0.016
0.049 0.015 0.011 0.016 0.012 0.019 0.007
0.011 0.004 0.008 0.007 0.008 0.006 0.004
50.001 50.001 0.156 0.023 0.122 0.002 0.068
0.013 0.012 0.010 0.010 0.010 0.012 0.010
Beta
*Obtained by linear regression analyses. Model 1 is unadjusted. Model 2 is adjusted for age, alcohol consumption, smoking status and physical activity. Model 3 is adjusted for variables in model 2 plus hypertension and diabetes.
tissue ability to modulate outflow resistance.37 However, the causal mechanism of the impact of obesity on IOP remains unclear. Moreover, the impact of weight loss on IOP has never been investigated. Even though obesity appears to be positively associated with IOP, it is unclear whether statistically weak associations may explain clinical significance. Moreover, the results of published studies are inconsistent with respect to ethnicity, gender, age and measurements of obesity. Focusing on obesity measurements, a study in an adult European cohort reported that higher IOP was positively associated with BMI, WC and waist-to-hip ratio in univariate analyses. Waist-to-hip ratio remained significantly associated with higher IOP in multivariate analysis even after adjusting for central corneal thickness (CCT), and systemic conditions including diabetes, hypertension and dyslipidemia.15 Both cross-sectional and longitudinal studies from a large Japanese population reported that IOP was significantly associated with obesity measured by BMI in multivariate analyses.16 A subsequent cross-sectional, populationbased study in a southwestern island of Japan reported that higher BMI was significantly associated with higher IOP in multivariate analyses.17 Meanwhile, a cross-sectional study of Taiwanese adults reported that BMI was positively associated with IOP in young adults, especially men; however,
WC was not significantly associated with IOP in most groups and was positively associated with IOP only in elderly men from multivariate analyses.18 In two studies of South Koreans investigating the associations between IOP and metabolic syndrome and its components, abdominal obesity was not significantly associated with IOP among men or women in multiple regression analyses.38,39 However, a study about the relationship between IOP and systemic health parameters indicated that BMI was positively related to IOP in men but not in women.39 Some aspects of our study provide unique information relative to prior studies on obesity and IOP. In particular, the present study focused on body composition parameters, such as total and regional body fat. In addition to BMI and WC, total fat mass, total and regional fat percentages in men and total fat mass and trunk fat percentage in women were positively associated with IOP in linear regression analyses. In multivariate regression analyses after adjusting for confounding factors, men with higher BMI, total body fat mass, and total and regional body fat percentages exhibited increasing trends in ORs for having high IOP. However, the trends regarding the risk of high IOP were not significantly associated with any body composition parameters in women. Adiposity is potentially pathological to the cardiovascular system via excessive fat mass mechanisms and/or adipocyte Current Eye Research
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Intraocular Pressure and Parameters of Obesity
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FIGURE 2 The prevalence of higher intraocular pressure (18 mmHg) according to quartile groups of obesity parameters. *p for trend 50.05.
and adipose tissue dysfunction. Proinflammatory adipokines secreted from fat mass promote insulin resistance and induce a direct catabolic effect on muscle. This vicious circle leads to further fat accumulation and metabolic derangement.40,41 Several previous epidemiological studies indicate that elevated body fat percentage is associated with increased risks of developing cardiometabolic dysregulation, metabolic syndrome and cardiovascular risk factors, which are associated with high IOP even in subjects with a normal BMI.42–44 Thus it might be important to assess adiposity as a measure of obesity in order to identify subjects with an increased risk of high IOP and cardiometabolic risk. Another noteworthy finding of the present study is sex disparities in the relationships between IOP and measurements of obesity. Sex differences in regional fat distribution might explain this finding. Men have more visceral fat, whereas women have more subcutaneous fat. Adipose tissue distributed in the abdominal viscera confers a greater cardiovascular risk than subcutaneous adipose tissue. The greater visceral fat distribution in men might affect IOP in the same manner as cardiovascular disorders that are known to be associated with high IOP.45 In addition, estrogen is suggested to play a major role in sex !
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differences. Besides influencing the mediation of the fat distribution, estrogen may exert an ocular hypotensive effect by influencing the aqueous production and outflow systems. Estrogen regulates smooth muscle tone and vascular resistance by augmenting the activity of endothelial-based nitric oxide synthase.46 These mechanisms may be the cause of the increased susceptibility of the effects of obesity on high IOP in men. However, the precise underlying mechanisms are not well understood. The present study has some limitations. First, the cross-sectional design preludes the deduction of cause-and-effect relationships between parameters of obesity and increased IOP. Second, we were not able to assess the degree of visceral adiposity or serum sex hormone levels of the subjects because of the limited data from the KNHANES. Third, we could not include CCT in the analysis by a lack of data in the KNHANES. CCT is one of the risk factors for openangle glaucoma and this may have impact on the measurement of IOP.47,48 The relationship between obesity parameters and IOP might have been more accurate if the CCT was adjusted as a confounding factor. Fourth, the use of DEXA scan leads to hydration-induced errors because lean tissue hydration varies systematically with fluid distribution.49
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TABLE 3 Odds ratios (95% confidence intervals) of having intraocular pressure 18 mmHg according to parameters of obesity. Men
Women
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
1 1.35 (1.03–1.76) 1.75 (1.41–2.18) 50.001
1 1.35 (1.04–1.76) 1.75 (1.41–2.18) 50.001
1 1.31 (1.00–1.71) 1.64 (1.31–2.05) 50.001
1 1.56 (1.23–1.98) 1.61 (1.27–2.03) 50.001
1 1.56 (1.22–1.99) 1.59 (1.23–2.05) 50.001
1 1.52 (1.19–1.94) 1.40 (1.09–1.79) 0.004
1 1.4 (1.14–1.71) 0.001
1 1.38 (1.12–1.69) 0.002
1 1.25 (1.01–1.55) 0.038
1 1.39 (1.13–1.72) 0.002
1 1.36 (1.08–1.71) 0.009
1 1.23 (0.98–1.54) 0.079
1 1.07 (0.79–1.46) 0.89 (0.66–1.21) 1.31 (0.97–1.76) 0.134
1 1.11 (0.81–1.52) 0.95 (0.69–1.31) 1.42 (1.03–1.94) 0.054
1 1.08 (0.79–1.48) 0.90 (0.65–1.23) 1.30 (0.95–1.79) 0.166
1 0.97 (0.74–1.28) 1.04 (0.80–1.36) 1.22 (0.93–1.60) 0.114
1 1.00 (0.76–1.32) 1.08 (0.83–1.40) 1.28 (0.97–1.68) 0.063
1 0.97 (0.73–1.30) 1.04 (0.80–1.35) 1.15 (0.88–1.51) 0.264
1 1.35 (1.02–1.78) 1.56 (1.19–2.04) 1.95 (1.50–2.54) 50.001
1 1.30 (0.98–1.72) 1.52 (1.16–2.00) 1.97 (1.51–2.57) 50.001
1 1.30 (0.98–1.73) 1.44 (1.09–1.89) 1.79 (1.36–2.36) 50.001
1 1.05 (0.79–1.4) 1.03 (0.77–1.37) 1.32 (0.98–1.77) 0.081
1 1.02 (0.77–1.36) 0.99 (0.74–1.32) 1.28 (0.95–1.73) 0.123
1 1.01 (0.76–1.35) 0.93 (0.69–1.25) 1.15 (0.85–1.55) 0.455
1 1.09 (0.83–1.43) 1.59 (1.22–2.07) 1.57 (1.18–2.09) 50.001
1 1.05 (0.80–1.39) 1.55 (1.19–2.02) 1.58 (1.18–2.10) 50.001
1 1.02 (0.77–1.34) 1.44 (1.11–1.88) 1.43 (1.06–1.91) 0.003
1 1.19 (0.90–1.57) 1.12 (0.83–1.50) 1.16 (0.85–1.60) 0.438
1 1.15 (0.87–1.52) 1.07 (0.79–1.45) 1.10 (0.8–1.52) 0.685
1 1.10 (0.83–1.47) 0.97 (0.71–1.32) 0.99 (0.72–1.37) 0.752
1 0.99 (0.74–1.33) 1.49 (1.15–1.93) 1.60 (1.23–2.09) 50.001
1 0.94 (0.70–1.26) 1.45 (1.12–1.88) 1.57 (1.19–2.05) 50.001
1 0.92 (0.68–1.23) 1.38 (1.05–1.79) 1.40 (1.05–1.86) 0.002
1 1.26 (0.94–1.68) 1.30 (0.97–1.73) 1.41 (1.03–1.92) 0.031
1 1.21 (0.90–1.63) 1.23 (0.91–1.67) 1.32 (0.94–1.86) 0.114
1 1.17 (0.86–1.59) 1.13 (0.83–1.53) 1.17 (0.83–1.65) 0.45
1 1.43 (1.07–1.9) 1.40 (1.05–1.86) 1.67 (1.24–2.26) 0.002
1 1.43 (1.07–1.90) 1.40 (1.05–1.86) 1.73 (1.28–2.34) 0.001
1 1.38 (1.03–1.86) 1.34 (1.01–1.79) 1.63 (1.20–2.22) 0.004
1 0.93 (0.72–1.21) 0.92 (0.71–1.20) 0.80 (0.60–1.08) 0.155
1 0.95 (0.73–1.24) 0.94 (0.72–1.23) 0.82 (0.61–1.10) 0.191
1 0.92 (0.71–1.21) 0.95 (0.72–1.24) 0.81 (0.60–1.10) 0.211
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Body mass index (kg/m ) 523 23–25 25 p for trend Waist circumference (cm) Men 590 or Women 580 Men 90 or Women 80 p for trend Total muscle mass Q1 Q2 Q3 Q4 p for trend Total fat mass Q1 Q2 Q3 Q4 p for trend Total fat percentage Q1 Q2 Q3 Q4 p for trend Trunk fat percentage Q1 Q2 Q3 Q4 p for trend Leg fat percentage Q1 Q2 Q3 Q4 p for trend
Model 1 is unadjusted. Model 2 is adjusted for age, alcohol consumption, smoking status and physical activity. Model 3 is adjusted for variables in model 2 plus hypertension and diabetes.
Particularly in conditions with altered fluid balance, such as cardiac and renal failure, it may vary in accuracy.50 However, individuals with heart failure were not considered in the analyses. Fifth, the ophthalmic information, such as CCT and gonioscopy data lacked in this study and IOP only lends one piece of information to the diagnosis of glaucoma, therefore the clinical relevance of our study findings to developing glaucoma remains to be determined from further studies. Lastly, we did not perform multiple comparisons because the objective of our study is to find associated factors using exploratory analyses based on nationally representative data. The exploratory analyses are regarded as a reasonable way to enhance detection of general trends.
Despite these limitations, this study has several strengths. As nationally representative data, the KNHANES enables us to evaluate the accurate health status of contemporary Koreans as a single ethnicity. Additionally, the study also assessed adiposity as parameter of obesity. In conclusion, IOP is positively associated with BMI, WC, total fat mass, and total and regional body fat percentages in men, and with BMI, WC, total fat mass, and trunk fat percentage in women. Increased BMI, WC, and total and regional body fat are positively associated with a risk of higher IOP (IOP 18 mmHg) especially in Korean men. Screening for obesity including adiposity may be helpful for identification of individuals at risk for high IOP. Current Eye Research
Intraocular Pressure and Parameters of Obesity
ACKNOWLEDGEMENTS We would like to greatly appreciate the participants in the 2008–2010 Korea National Health and Nutrition Examination Survey.
DECLARATION OF INTEREST The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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