Acta Ophthalmologica 2014
The distribution of intraocular pressure and associated systemic factors in a Korean population: The Korea National Health and Nutrition Examination Survey Mi Jeung Kim,1,2 Ki Ho Park,1,2 Chan Yun Kim,3 Jin Wook Jeoung1,2 and Seok Hwan Kim1,4 1
Department Department 3 Department 4 Department 2
of of of of
Ophthalmology, Ophthalmology, Ophthalmology, Ophthalmology,
Seoul National University College of Medicine, Seoul, Korea Seoul National University Hospital, Seoul, Korea Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea Seoul National University Boramae Hospital, Seoul, Korea
ABSTRACT. Purpose: To investigate the distribution of intraocular pressure (IOP) and its associated factors in a large Korean population based on the data from the nationwide cross-sectional survey. Methods: We obtained 2009–2010 data from the Korea National Health and Nutrition Examination Survey (KNHANES) (n = 17 901). After excluding individuals under 19 years of age, a total of 13 431 subjects were enrolled. All participants completed a comprehensive questionnaire and underwent an ocular examination including measurement of IOP by Goldmann applanation tonometry, as well as a systemic evaluation including blood pressure measurements, anthropometry and blood tests. Results: The mean IOP in the right eye was 13.99 2.75 mmHg, and in the left eye, 13.99 2.75 mmHg, representing no significant bilateral difference. There was, however, a significant difference between males (14.19 2.78 mmHg) and females (13.79 2.70 mmHg) (p < 0.001). Multiple regression analysis revealed that higher IOP was significantly correlated with male sex, higher myopic refractive error, higher body mass index, higher systolic blood pressure, higher fasting plasma glucose and higher total cholesterol (all p < 0.05). On the other hand, age, histories of smoking or migraine or cold hands/feet were not significantly correlated with IOP (all p > 0.05). Conclusions: In the general Korean population, IOP increases with male sex and increasing myopia. Further, IOP is significantly correlated with systemic factors relating to cardiovascular disease and metabolic syndrome. Key words: epidemiology – intraocular pressure – Korean population – nationwide cross-sectional survey – systemic factors
Acta Ophthalmol. 2014: 92: e507–e513 ª 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd
doi: 10.1111/aos.12327
Introduction Intraocular pressure (IOP), one of the most important risk factors for the development and progression of glau-
coma (Gazzard et al. 2003; Musch et al. 2011), is associated with various systemic and ocular factors (Wu & Leske 1997; Weih et al. 2001; Lee et al. 2002; Yip et al. 2007; Kawase et al. 2008; Tomoyose et al. 2010; Jonas
et al. 2011). For effective control of IOP, it is important to understand not only its distribution in a given population but also those associated factors in clinical situations. A number of epidemiologic studies have carried out just this (Klein et al. 1992; Leske et al. 1997; Hashemi et al. 2005; Xu et al. 2005; Fukuoka et al. 2008; Kawase et al. 2008; Tomoyose et al. 2010; Jonas et al. 2011; Suh & Kee 2012). However, the results vary in different geographical areas and ethnic groups. For example, with respect to IOP distribution, several Asian populationbased studies have shown lower mean IOP ranges than have been revealed for American populations (Sommer et al. 1991; Klein et al. 1992; Iwase et al. 2004; Xu et al. 2005; Yip et al. 2007). Not only IOP distribution but also its correlations with ocular or systemic factors have shown different results for different races and regions. Several population-based studies conducted in Western countries have shown a positive correlation between IOP and age. By contrast, East Asian studies have found a negative correlation between IOP and age. Considering all of these discrepancies, it is mandatory to investigate the distribution of IOP and its related variables in each population. In Korea, there have been few reports of the distribution of IOP in general population (Kim et al. 2011; Suh & Kee 2012). These studies, however,
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Acta Ophthalmologica 2014
included only the population with ages over 40 or 50 years from 2009 till 2010. The purpose of this study was to investigate, on the basis of the data of the nationwide Korea National Health and Nutrition Examination Survey (KNHANES), the distribution of IOP and its association with various systemic factors in a large Korean population. This is the first and largest government-initiated study on nationwide 19 years and older Korean IOP distribution as measured by GAT.
Materials and Methods Study population
The KNHANES is a population-based, cross-sectional epidemiological survey conducted in Korea. It includes a health interview survey, a health behaviour survey, a nutrition survey and a health examination study. The KNHANES has been conducted periodically since 1998 by the Korean Ministry of Health and Welfare to collect comprehensive information on the health and nutritional status of the nationwide population, using a rolling sampling design involving a complex, stratified, multistage, probability-cluster survey of a representative sample of a non-institutionalized population (Yoon et al. 2004; Choi et al. 2011; Kim & Lee 2012). The ophthalmologic component of the survey was conducted over the course of 5 years from 2008 to 2013. The present study was based on data collected during the third and final year (2009) of KNHANES IV (2007–2009) and the first year (2010) of KNHANES V (2010–2012). For KNHANES IV, the subject population was enrolled on the basis of the 2005 Population and Housing National Census Registry, which divided the entire nation into 11 geographical areas each stratified into 29 regional subunits according to administrative district (Dong, Eup and Myeon) and housing type with consideration of the age composition. For each survey year, 200 administrative districts were selected as primary sampling units. From each administrative district, one enumeration district was extracted for use as secondary sampling units representing regional characteristics. Finally, from each
e508
enumeration district, 23 survey households were selected. In KNHANES V (2010–2012), the subject population was recruited from both non-apartment and apartment residents. As primary sampling units, enumeration districts were extracted, as based on the administrative district (Tong, Ban, and Ri) for non-apartment residents and the apartment complex for apartment residents. As secondary sampling units, 20 households were selected for each enumeration district. In this manner, 3975 households representing 200 enumeration districts were enrolled in 2009, and 3840 households for 192 enumeration districts in 2010. All of the members of each selected household were asked to participate in the surveys. In the end, there were 9760 participants in 2009 and 8141 in 2010. A total of 13 431 participants, aged 19 years or older, were enrolled in this study. This study was performed in accordance with the tenets of the Declaration of Helsinki for the use of human subjects in biomedical research and in accordance with regional laws regarding maintenance of the privacy of patient data. General medical examinations
All health examinations and interviews were conducted by trained teams in mobile centres, while the nutrition surveys were performed in individual households. Anthropometric parameters were obtained by standardized methods. Height and body weight were measured with a stadiometer and a balanced scale, respectively. Waist circumference was measured at the midpoint between the bottom of the rib cage and the top of the lateral border of the iliac crest during minimal respiration. The body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Blood pressure was acquired using a mercury sphygmomanometer with the subject in the sitting position after a 10-min rest period. Two measurements were taken from all the subjects at 5-min intervals, and the average of the two measurements was used. Blood pressure was defined as the difference between systolic and diastolic blood pressure. Biochemistry tests of blood and urine were performed. The blood biochemical parameters associated with diabetes (fasting plasma glucose, hae-
moglobin A1c, insulin), dyslipidemia (total cholesterol, HDL-cholesterol, triglyceride, LDL-cholesterol), liver function (GOT, GPT, cGT), anemia (haemoglobin, hematocrit, ferritin, iron), renal function (BUN, Creatinine), bone metabolism (vitamin D, alkaline phosphatase, parathyroid hormone), heavy metals (lead, mercury, cadmium) and complete blood cell count were analysed by a central, certified laboratory. Medical interviews
A standardized questionnaire was administered for assessment of overall general medical condition, including history of ocular disease, systemic disease, surgery, trauma, current medication, smoking habit, alcohol intake, physical activity and occupation. Family history of glaucoma also was recorded. Ocular examinations
Complete ocular examinations were performed by ophthalmologists usually in the morning. The measured parameters were as follows. Uncorrected visual acuity (UCVA) and/or best-corrected visual acuity (BCVA) on the Log Mar Scale, using an international standard vision chart (Jin’s Vision Chart, Seoul, Korea) at a distance of 4 m; refractive errors measured by autorefractor / keratometer (KR8800; Topcon, Tokyo, Japan); slit-lamp examination, including assessment of peripheral anterior chamber depth (PACD) by the Van Herick method (Haag-Streit model BQ-900; Haag-Streit AG, Koeniz, Switzerland); fundus photography by digital non-mydriatic fundus camera (TRC-NW6S; Topcon, Tokyo, Japan, and Nikon D-80 digital camera; Nikon, Tokyo, Japan). IOP was measured by trained ophthalmologists using Goldmann applanation tonometry (GAT; Haag-Streit; Haag-Streit AG, Koeniz, Switzerland), once for each eye from right to left, prior to the perimetry and fundus photography. Additionally, for subjects who had elevated IOP (≥22 mmHg) or a glaucomatous optic disc, a visual field test using frequency-doubling technology perimetry (Humphrey Matrix; Carl Zeiss Meditec Inc., Dublin, CA, USA) with the screening programme N-30-1 was performed.
Acta Ophthalmologica 2014
Statistical analysis
The statistical analyses were performed using SAS software (version 9.2; SAS Institute, Cary, NC, USA), and specified procedures for complex sampling design with the ‘proc survey’ commands for survey data were applied. A complex sample analysis was performed with reference to the weight, stratification variance and cluster variance, following the statistical guidelines of the Korea Centers for Disease Control and Prevention. The weight indicated the probability of being sampled, which, to adjust for age and sex, was determined on the basis of the sampling rate and the response rate. All mean values and percentages were weighted to be nationally representative. The associations between IOP and various systemic and ocular factors were analysed by univariate linear regression analysis and dummy variable regression analysis, respectively. Based on the results of the univariate regression analysis, a multivariate regression analysis was performed with stepwise selection of explanatory variables (probability of F to enter, 0.050; probability of F to remove, 0.100) including sex, BMI, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, HDL-cholesterol, triglyceride, self-reported history of smoking, PACD, spherical equivalent and lens status. Because, with clinical consideration, age was possible to significantly correlate with IOP, we also included it in multivariate analysis. Excluded from the multivariate regression analysis were the variables haemoglobin A1c, LDL-cholesterol, selfreported history of refractive surgery or glaucoma surgery, and current treatment for glaucoma, due to significant numbers of missing values. A p-value < 0.05 was considered to be significant.
43.62 15.81 years; p < 0.0001, Student’s t-test), and women were more common among participants than nonparticipants (respective male-to-female ratios: 6007:7883 versus 2294:1878; p < 0.0001, chi-square test) (Table S1). Of these 17 901 participants, 4116 who were younger than 19 years were excluded. Also excluded were 354 participants who had missing PACD data. Of the 26 862 eyes of the 13 431 remaining subjects, three right eyes and four left eyes were excluded due to missing IOP data, which eliminated a further two subjects. Thus, finally, a total of 13 428 right eyes and 13 427 left eyes of 13 429 subjects (both eyes were eligible in 13 426 subjects) were enrolled in the study. The mean IOP in the right eye was 13.99 2.75 mmHg (95% confidence interval [CI], 13.85–14.12 mmHg) and in the left eye, 13.99 2.75 mmHg (95% CI, 13.85–14.12 mmHg). This difference was not statistically significant (95% CI, 0.05–0.05 mmHg), and the IOP in the right eye was significantly correlated with that in the left (p < 0.001; r = 0.830; Fig. 1). Given these facts, only the right-eye data were analysed in the following assessment. The demographic and baseline characteristics of the 13 428 right-eye-eligible subjects are provided in Table 1.
Distribution of intraocular pressure (IOP)
The frequency distribution of the righteye IOP of the total 13 428 subjects and each sex group (male subjects; n = 5799, female subjects; n = 5799) followed a mostly Gaussian distribution Fig. 2. Table 2 shows the mean right-eye IOP as listed by age and sex. There was a significant difference between male subjects (14.19 2.78 mmHg, n = 5799) and female subjects (13.79 2.70 mmHg, n = 5799) (p < 0.001). Table 3, meanwhile, shows the prevalence of ‘high IOP’, defined as ≥22 mmHg or ≥99.5th percentile, by age and sex. IOP-associated factors
A univariate linear regression analysis was performed to identify the linear trends of the continuous variables. The IOP increased in higher anthropometric parameters (height, weight, BMI), higher systolic and diastolic blood pressure, higher fasting glucose, higher lipid parameters excluding HDL-cholesterol (total cholesterol, triglyceride, LDLcholesterol), and higher refractive errors (all p < 0.05). However, IOP decreased in higher HDL-cholesterol (p = 0.01). A dummy variable regression analysis was performed to determine the
Results Demographic characteristics
Of the total target population of 23 660, 17 901 participants (response rate: 75.7%) underwent the examinations and the medical interviews (9760 participants of the 12 722 target population (76.7%) in 2009, and 8141 of the 10 938 (74.4%) in 2010). The KNHANES participants were older than the non-participants (49.35 16.62 years versus
Fig. 1. Scatter plot of correlation between intraocular pressure (IOP) in right eye and left eye. Among the 13426 subjects in whom both eyes were eligible, the IOP in the right eye was significantly correlated with that in the left eye (Pearson correlation coefficient, 0.830, p < 0.001).
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Table 1. Demographic and baseline characteristics of 13 428 enrolled right-eye-eligible subjects. Parameters
Mean SD
Age (years) Height (cm) Weight (kg) Waist circumference (cm) Body mass index (kg ⁄ m2) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Fasting plasma glucose (mg/dl) Haemoglobin A1c (%) Total Cholesterol (mg/dl) HDL-Cholesterol – correction value (mg/dl) Triglyceride (mg/dl) LDL-Cholesterol (mg/dl) Data from right eye Intraocular pressure (mmHg)† Spherical equivalents (dioptres)
49.35 162.09 62.26 81.05 23.62 118.12 74.86 97.52 7.31 187.53 48.01 133.53 112.12
16.55 9.34 11.59 11.76 3.38 17.71 10.52 22.87 1.46 36.17 10.85 110.39 31.57
13.99 2.75 0.9 2.28 Number of subjects (%)
Parameters Male:Female History of hypertension History of diabetes History of migraine or cold hands/feet History of coronary heart disease History of cerebrovascular disease History of dyslipidemia History of smoking Data from right eye Peripheral anterior chamber ≤1/4 Corneal thickness (CT) depth (PACD) 1/4 CT < PACD ≤ 1/2 CT >1/2 CT Lens status Cataract Normal crystalline lens Pseudophakia Aphakia
5799 (43.19):7629 (56.81) 2823 (21.02) 1036 (7.72) 5704 (42.5) 296 (2.22) 247 (1.85) 1153 (8.65) 5530 (41.58) 132 (0.98) 2839 (21.14) 10457 (77.87) 3619 (26.95) 9140 (68.07) 662 (4.93) 6 (0.04)
† Mean IOP value was weighted by the specified procedures for the complex sampling design with the ‘proc survey’.
influence of categorical variables on IOP. In the results, IOP was positively correlated with the male sex, history of hypertension, history of diabetes, history of dyslipidemia and history of smoking, whereas it was negatively
correlated with history of migraine or cold hands/feet (all p < 0.05). The parameters not significantly correlated with IOP were age, weight circumference, history of cardiovascular disease, history of cerebral vascular disease,
PACD grading, and lens status (all p > 0.05) (Table 4). A multiple regression analysis was performed to assess the multifactorial influence on IOP, including that of various parameters that were found to be significantly associated with IOP in the univariate analysis. In order to rule out multicollinearity, some of the parameters that were significantly correlated with other parameters were excluded. For example, height and weight which were closely correlated with BMI (weight [kg]/height [m]2) were excluded, whereas BMI, as an independent parameter, was included. Table 5 presents the results of multiple regression analysis. As is apparent, IOP was negatively correlated with refractive errors (b = 0.105, p < 0.0001), adjusting for age, sex and other possible confounding variables. It was positively correlated with BMI (b = 0.040, p < 0.0001), systolic blood pressure (b = 0.012, p = 0.001), fasting plasma glucose (b = 0.006, p < 0.0001) and total cholesterol (b = 0.003, p < 0.0001), adjusting for age, sex and other confounding variables. The male sex was positively correlated with IOP (b = 0.190, p = 0.032). In contrast to the univariate analysis findings, diastolic blood pressure, history of smoking, migraine or cold hands/feet were not significantly correlated with IOP (all p > 0.05). Age also was not significantly correlated with IOP (p = 0.298).
Discussion The present study represents the largest population-based survey (total participants: 13 431) in Korea for assessment of GAT-measured IOP distribution. The survey was conducted nationwide
Fig. 2. Overall distribution of intraocular pressure (IOP) among 13 428 subjects. (A) Distribution of IOP among total 13428 subjects. (B) Distribution of IOP among male subjects (n = 5799). (C) Distribution of IOP among female subjects (n = 7629). *All of the data are from the right eye.
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Table 2. Mean intraocular pressure (IOP) in the right eye as stratified by age and sex. Subgroup
Age (years)
Mean SD
Males (n = 5799)
≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70
13.94 14.29 14.44 14.36 13.94 13.72 13.58 13.80 13.83 13.90 14.01 13.69 13.77 14.05 14.14 14.13 13.97 13.70
Females (n = 7629)
Total (n = 13428)
2.70 2.70 2.80 2.89 2.79 2.79 2.71 2.68 2.67 2.68 2.62 2.84 2.71 2.70 2.75 2.80 2.70 2.82
95% Confidence interval
n
13.69–14.19 14.04–14.53 14.20–14.67 14.12–14.60 13.71–14.16 13.45–13.99 13.34–13.82 13.60–14.00 13.63–14.03 13.70–14.09 13.79–14.23 13.42–13.95 13.56–13.97 13.86–14.24 13.95–14.32 13.95–14.31 13.79–14.15 13.48–13.92
767 1085 1153 997 998 799 990 1509 1461 1380 1196 1093 1757 2594 2614 2377 2194 1892
Table 3. Prevalence of high intraocular pressure (IOP)† in right eye as stratified by age and sex. Subjects
Age (years)
IOP ≥ 22 mmHg n (%)
IOP ≥ 99.5th percentile n (%)
Males (n = 5799)
≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70
1 1 5 4 3 4 2 1 4 2 2 6 3 2 9 6 5 10
7 10 16 13 8 7 2 6 6 9 4 13 9 16 22 22 12 20
Females (n = 7629)
Total (n = 13428)
†
(0.10) (0.09) (0.56) (0.44) (0.26) (0.54) (0.21) (0.05) (0.36) (0.14) (0.12) (0.38) (0.16) (0.07) (0.46) (0.29) (0.19) (0.44)
(0.78) (1.06) (1.48) (1.51) (0.87) (1.42) (0.21) (0.43) (0.53) (0.65) (0.31) (1.30) (0.51) (0.75) (1.01) (1.08) (0.58) (1.34)
High IOP was defined as IOP ≥ 22 mmHg or IOP ≥ 99.5th percentile.
to minimize the effects of regional IOP variability which has been reported by (Suh & Kee 2012). The mean IOP was 13.99 2.75 mmHg, which was similar to that reported previously of a Korean population (14.10 2.74 mmHg) (Suh & Kee 2012). However, it is lower than those of previous reports from other Asian countries (14.6 2.7 mmHg in Tajimi study; 16.11 3.39 mmHg in Beijing eye study; 14.54 0.11 mmHg in Tanjong Pagar study (Iwase et al. 2004; Xu et al. 2005; Yip et al. 2007)). The explanation for our lower IOP is unclear, although just the specific population difference, or some other, unknown factor, might have been sufficient.
In the frequency distribution for IOP, even numbers generally were more common than odd (Fig. 2). We speculated that this reflected a kind of GAT-dial digit preference known as the ‘hedgehog effect’ (Buller et al. 2005) and that it might diminish the statistical power of the present study (Hessel 1986). We found a significantly higher mean mean IOP among males (14.19 2.78 mmHg) than females (13.79 2.70 mmHg). Moreover, adjusting for age and other confounding variables, the male sex showed a significantly positive correlation with IOP. On the contrary, another Korean study found no significant intergender difference in IOP (Suh &
Kee 2012). Certainly, IOP difference by sex remains controversial; possible cause of the divergent results to-date ismultiple confoundingfactors (e.g.geneticfactors, metabolic factors). With respect to age, our IOP readings were not significantly correlated with it, adjusting for potential confounding factors. This conflicted with previous East Asian studies conducted in Korea (Suh & Kee 2012) and Japan (Shiose 1984; Kawase et al. 2008; Tomoyose et al. 2010), which showed a negative correlation between IOP and age. Some reports have ascribed the discrepancies in age-related IOP changes to differences in age-related systemic changes including BMI and blood pressure (Shiose 1984; Shiose & Kawase 1986; Tomoyose et al. 2010). In this light, we supposed that IOP was not significantly correlated with age in our results, because precisely adjusting for various systemic factors including BMI and blood pressure was performed. Meanwhile, in the comparison of the mean IOP among the age groups subdivided by 10 years, the values increased between 19 and 49 years, peaking for the 40–49 years group. Then, the mean IOP decreased for the subjects above 50 years of age (Table 2). These findings carry significance in that there have been few reports on IOP among young adults ( 1/2 CT ref: Lens: Normal crystalline lens (n = 9141) Cataract (n = 3621) Pseudophakia (n = 662) Aphakia (n = 6)
Coefficient b 0.0004 0.396 0.007 0.020 0.010 0.076 0.016 0.027 0.241 0.010 0.038 0.487 0.005 0.007 0.005 0.002 0.329 0.242 0.109 0.381 0.268 0.085 1 0.280 0.067 1 0.029 0.089 0.828
Standard error
p-value
0.002 0.052 0.003 0.003 0.007 0.009 0.002 0.003 0.073 0.001 0.064 0.112 0.001 0.003 0.002 0.000 0.099 0.058 0.209 0.201 0.059 0.015
0.841†
The distribution of intraocular pressure and associated systemic factors in a Korean population: The Korea National Health and Nutrition Examination Survey Mi Jeung Kim,1,2 Ki Ho Park,1,2 Chan Yun Kim,3 Jin Wook Jeoung1,2 and Seok Hwan Kim1,4 1
Department Department 3 Department 4 Department 2
of of of of
Ophthalmology, Ophthalmology, Ophthalmology, Ophthalmology,
Seoul National University College of Medicine, Seoul, Korea Seoul National University Hospital, Seoul, Korea Institute of Vision Research, Yonsei University College of Medicine, Seoul, Korea Seoul National University Boramae Hospital, Seoul, Korea
ABSTRACT. Purpose: To investigate the distribution of intraocular pressure (IOP) and its associated factors in a large Korean population based on the data from the nationwide cross-sectional survey. Methods: We obtained 2009–2010 data from the Korea National Health and Nutrition Examination Survey (KNHANES) (n = 17 901). After excluding individuals under 19 years of age, a total of 13 431 subjects were enrolled. All participants completed a comprehensive questionnaire and underwent an ocular examination including measurement of IOP by Goldmann applanation tonometry, as well as a systemic evaluation including blood pressure measurements, anthropometry and blood tests. Results: The mean IOP in the right eye was 13.99 2.75 mmHg, and in the left eye, 13.99 2.75 mmHg, representing no significant bilateral difference. There was, however, a significant difference between males (14.19 2.78 mmHg) and females (13.79 2.70 mmHg) (p < 0.001). Multiple regression analysis revealed that higher IOP was significantly correlated with male sex, higher myopic refractive error, higher body mass index, higher systolic blood pressure, higher fasting plasma glucose and higher total cholesterol (all p < 0.05). On the other hand, age, histories of smoking or migraine or cold hands/feet were not significantly correlated with IOP (all p > 0.05). Conclusions: In the general Korean population, IOP increases with male sex and increasing myopia. Further, IOP is significantly correlated with systemic factors relating to cardiovascular disease and metabolic syndrome. Key words: epidemiology – intraocular pressure – Korean population – nationwide cross-sectional survey – systemic factors
Acta Ophthalmol. 2014: 92: e507–e513 ª 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd
doi: 10.1111/aos.12327
Introduction Intraocular pressure (IOP), one of the most important risk factors for the development and progression of glau-
coma (Gazzard et al. 2003; Musch et al. 2011), is associated with various systemic and ocular factors (Wu & Leske 1997; Weih et al. 2001; Lee et al. 2002; Yip et al. 2007; Kawase et al. 2008; Tomoyose et al. 2010; Jonas
et al. 2011). For effective control of IOP, it is important to understand not only its distribution in a given population but also those associated factors in clinical situations. A number of epidemiologic studies have carried out just this (Klein et al. 1992; Leske et al. 1997; Hashemi et al. 2005; Xu et al. 2005; Fukuoka et al. 2008; Kawase et al. 2008; Tomoyose et al. 2010; Jonas et al. 2011; Suh & Kee 2012). However, the results vary in different geographical areas and ethnic groups. For example, with respect to IOP distribution, several Asian populationbased studies have shown lower mean IOP ranges than have been revealed for American populations (Sommer et al. 1991; Klein et al. 1992; Iwase et al. 2004; Xu et al. 2005; Yip et al. 2007). Not only IOP distribution but also its correlations with ocular or systemic factors have shown different results for different races and regions. Several population-based studies conducted in Western countries have shown a positive correlation between IOP and age. By contrast, East Asian studies have found a negative correlation between IOP and age. Considering all of these discrepancies, it is mandatory to investigate the distribution of IOP and its related variables in each population. In Korea, there have been few reports of the distribution of IOP in general population (Kim et al. 2011; Suh & Kee 2012). These studies, however,
e507
Acta Ophthalmologica 2014
included only the population with ages over 40 or 50 years from 2009 till 2010. The purpose of this study was to investigate, on the basis of the data of the nationwide Korea National Health and Nutrition Examination Survey (KNHANES), the distribution of IOP and its association with various systemic factors in a large Korean population. This is the first and largest government-initiated study on nationwide 19 years and older Korean IOP distribution as measured by GAT.
Materials and Methods Study population
The KNHANES is a population-based, cross-sectional epidemiological survey conducted in Korea. It includes a health interview survey, a health behaviour survey, a nutrition survey and a health examination study. The KNHANES has been conducted periodically since 1998 by the Korean Ministry of Health and Welfare to collect comprehensive information on the health and nutritional status of the nationwide population, using a rolling sampling design involving a complex, stratified, multistage, probability-cluster survey of a representative sample of a non-institutionalized population (Yoon et al. 2004; Choi et al. 2011; Kim & Lee 2012). The ophthalmologic component of the survey was conducted over the course of 5 years from 2008 to 2013. The present study was based on data collected during the third and final year (2009) of KNHANES IV (2007–2009) and the first year (2010) of KNHANES V (2010–2012). For KNHANES IV, the subject population was enrolled on the basis of the 2005 Population and Housing National Census Registry, which divided the entire nation into 11 geographical areas each stratified into 29 regional subunits according to administrative district (Dong, Eup and Myeon) and housing type with consideration of the age composition. For each survey year, 200 administrative districts were selected as primary sampling units. From each administrative district, one enumeration district was extracted for use as secondary sampling units representing regional characteristics. Finally, from each
e508
enumeration district, 23 survey households were selected. In KNHANES V (2010–2012), the subject population was recruited from both non-apartment and apartment residents. As primary sampling units, enumeration districts were extracted, as based on the administrative district (Tong, Ban, and Ri) for non-apartment residents and the apartment complex for apartment residents. As secondary sampling units, 20 households were selected for each enumeration district. In this manner, 3975 households representing 200 enumeration districts were enrolled in 2009, and 3840 households for 192 enumeration districts in 2010. All of the members of each selected household were asked to participate in the surveys. In the end, there were 9760 participants in 2009 and 8141 in 2010. A total of 13 431 participants, aged 19 years or older, were enrolled in this study. This study was performed in accordance with the tenets of the Declaration of Helsinki for the use of human subjects in biomedical research and in accordance with regional laws regarding maintenance of the privacy of patient data. General medical examinations
All health examinations and interviews were conducted by trained teams in mobile centres, while the nutrition surveys were performed in individual households. Anthropometric parameters were obtained by standardized methods. Height and body weight were measured with a stadiometer and a balanced scale, respectively. Waist circumference was measured at the midpoint between the bottom of the rib cage and the top of the lateral border of the iliac crest during minimal respiration. The body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Blood pressure was acquired using a mercury sphygmomanometer with the subject in the sitting position after a 10-min rest period. Two measurements were taken from all the subjects at 5-min intervals, and the average of the two measurements was used. Blood pressure was defined as the difference between systolic and diastolic blood pressure. Biochemistry tests of blood and urine were performed. The blood biochemical parameters associated with diabetes (fasting plasma glucose, hae-
moglobin A1c, insulin), dyslipidemia (total cholesterol, HDL-cholesterol, triglyceride, LDL-cholesterol), liver function (GOT, GPT, cGT), anemia (haemoglobin, hematocrit, ferritin, iron), renal function (BUN, Creatinine), bone metabolism (vitamin D, alkaline phosphatase, parathyroid hormone), heavy metals (lead, mercury, cadmium) and complete blood cell count were analysed by a central, certified laboratory. Medical interviews
A standardized questionnaire was administered for assessment of overall general medical condition, including history of ocular disease, systemic disease, surgery, trauma, current medication, smoking habit, alcohol intake, physical activity and occupation. Family history of glaucoma also was recorded. Ocular examinations
Complete ocular examinations were performed by ophthalmologists usually in the morning. The measured parameters were as follows. Uncorrected visual acuity (UCVA) and/or best-corrected visual acuity (BCVA) on the Log Mar Scale, using an international standard vision chart (Jin’s Vision Chart, Seoul, Korea) at a distance of 4 m; refractive errors measured by autorefractor / keratometer (KR8800; Topcon, Tokyo, Japan); slit-lamp examination, including assessment of peripheral anterior chamber depth (PACD) by the Van Herick method (Haag-Streit model BQ-900; Haag-Streit AG, Koeniz, Switzerland); fundus photography by digital non-mydriatic fundus camera (TRC-NW6S; Topcon, Tokyo, Japan, and Nikon D-80 digital camera; Nikon, Tokyo, Japan). IOP was measured by trained ophthalmologists using Goldmann applanation tonometry (GAT; Haag-Streit; Haag-Streit AG, Koeniz, Switzerland), once for each eye from right to left, prior to the perimetry and fundus photography. Additionally, for subjects who had elevated IOP (≥22 mmHg) or a glaucomatous optic disc, a visual field test using frequency-doubling technology perimetry (Humphrey Matrix; Carl Zeiss Meditec Inc., Dublin, CA, USA) with the screening programme N-30-1 was performed.
Acta Ophthalmologica 2014
Statistical analysis
The statistical analyses were performed using SAS software (version 9.2; SAS Institute, Cary, NC, USA), and specified procedures for complex sampling design with the ‘proc survey’ commands for survey data were applied. A complex sample analysis was performed with reference to the weight, stratification variance and cluster variance, following the statistical guidelines of the Korea Centers for Disease Control and Prevention. The weight indicated the probability of being sampled, which, to adjust for age and sex, was determined on the basis of the sampling rate and the response rate. All mean values and percentages were weighted to be nationally representative. The associations between IOP and various systemic and ocular factors were analysed by univariate linear regression analysis and dummy variable regression analysis, respectively. Based on the results of the univariate regression analysis, a multivariate regression analysis was performed with stepwise selection of explanatory variables (probability of F to enter, 0.050; probability of F to remove, 0.100) including sex, BMI, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, HDL-cholesterol, triglyceride, self-reported history of smoking, PACD, spherical equivalent and lens status. Because, with clinical consideration, age was possible to significantly correlate with IOP, we also included it in multivariate analysis. Excluded from the multivariate regression analysis were the variables haemoglobin A1c, LDL-cholesterol, selfreported history of refractive surgery or glaucoma surgery, and current treatment for glaucoma, due to significant numbers of missing values. A p-value < 0.05 was considered to be significant.
43.62 15.81 years; p < 0.0001, Student’s t-test), and women were more common among participants than nonparticipants (respective male-to-female ratios: 6007:7883 versus 2294:1878; p < 0.0001, chi-square test) (Table S1). Of these 17 901 participants, 4116 who were younger than 19 years were excluded. Also excluded were 354 participants who had missing PACD data. Of the 26 862 eyes of the 13 431 remaining subjects, three right eyes and four left eyes were excluded due to missing IOP data, which eliminated a further two subjects. Thus, finally, a total of 13 428 right eyes and 13 427 left eyes of 13 429 subjects (both eyes were eligible in 13 426 subjects) were enrolled in the study. The mean IOP in the right eye was 13.99 2.75 mmHg (95% confidence interval [CI], 13.85–14.12 mmHg) and in the left eye, 13.99 2.75 mmHg (95% CI, 13.85–14.12 mmHg). This difference was not statistically significant (95% CI, 0.05–0.05 mmHg), and the IOP in the right eye was significantly correlated with that in the left (p < 0.001; r = 0.830; Fig. 1). Given these facts, only the right-eye data were analysed in the following assessment. The demographic and baseline characteristics of the 13 428 right-eye-eligible subjects are provided in Table 1.
Distribution of intraocular pressure (IOP)
The frequency distribution of the righteye IOP of the total 13 428 subjects and each sex group (male subjects; n = 5799, female subjects; n = 5799) followed a mostly Gaussian distribution Fig. 2. Table 2 shows the mean right-eye IOP as listed by age and sex. There was a significant difference between male subjects (14.19 2.78 mmHg, n = 5799) and female subjects (13.79 2.70 mmHg, n = 5799) (p < 0.001). Table 3, meanwhile, shows the prevalence of ‘high IOP’, defined as ≥22 mmHg or ≥99.5th percentile, by age and sex. IOP-associated factors
A univariate linear regression analysis was performed to identify the linear trends of the continuous variables. The IOP increased in higher anthropometric parameters (height, weight, BMI), higher systolic and diastolic blood pressure, higher fasting glucose, higher lipid parameters excluding HDL-cholesterol (total cholesterol, triglyceride, LDLcholesterol), and higher refractive errors (all p < 0.05). However, IOP decreased in higher HDL-cholesterol (p = 0.01). A dummy variable regression analysis was performed to determine the
Results Demographic characteristics
Of the total target population of 23 660, 17 901 participants (response rate: 75.7%) underwent the examinations and the medical interviews (9760 participants of the 12 722 target population (76.7%) in 2009, and 8141 of the 10 938 (74.4%) in 2010). The KNHANES participants were older than the non-participants (49.35 16.62 years versus
Fig. 1. Scatter plot of correlation between intraocular pressure (IOP) in right eye and left eye. Among the 13426 subjects in whom both eyes were eligible, the IOP in the right eye was significantly correlated with that in the left eye (Pearson correlation coefficient, 0.830, p < 0.001).
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Table 1. Demographic and baseline characteristics of 13 428 enrolled right-eye-eligible subjects. Parameters
Mean SD
Age (years) Height (cm) Weight (kg) Waist circumference (cm) Body mass index (kg ⁄ m2) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Fasting plasma glucose (mg/dl) Haemoglobin A1c (%) Total Cholesterol (mg/dl) HDL-Cholesterol – correction value (mg/dl) Triglyceride (mg/dl) LDL-Cholesterol (mg/dl) Data from right eye Intraocular pressure (mmHg)† Spherical equivalents (dioptres)
49.35 162.09 62.26 81.05 23.62 118.12 74.86 97.52 7.31 187.53 48.01 133.53 112.12
16.55 9.34 11.59 11.76 3.38 17.71 10.52 22.87 1.46 36.17 10.85 110.39 31.57
13.99 2.75 0.9 2.28 Number of subjects (%)
Parameters Male:Female History of hypertension History of diabetes History of migraine or cold hands/feet History of coronary heart disease History of cerebrovascular disease History of dyslipidemia History of smoking Data from right eye Peripheral anterior chamber ≤1/4 Corneal thickness (CT) depth (PACD) 1/4 CT < PACD ≤ 1/2 CT >1/2 CT Lens status Cataract Normal crystalline lens Pseudophakia Aphakia
5799 (43.19):7629 (56.81) 2823 (21.02) 1036 (7.72) 5704 (42.5) 296 (2.22) 247 (1.85) 1153 (8.65) 5530 (41.58) 132 (0.98) 2839 (21.14) 10457 (77.87) 3619 (26.95) 9140 (68.07) 662 (4.93) 6 (0.04)
† Mean IOP value was weighted by the specified procedures for the complex sampling design with the ‘proc survey’.
influence of categorical variables on IOP. In the results, IOP was positively correlated with the male sex, history of hypertension, history of diabetes, history of dyslipidemia and history of smoking, whereas it was negatively
correlated with history of migraine or cold hands/feet (all p < 0.05). The parameters not significantly correlated with IOP were age, weight circumference, history of cardiovascular disease, history of cerebral vascular disease,
PACD grading, and lens status (all p > 0.05) (Table 4). A multiple regression analysis was performed to assess the multifactorial influence on IOP, including that of various parameters that were found to be significantly associated with IOP in the univariate analysis. In order to rule out multicollinearity, some of the parameters that were significantly correlated with other parameters were excluded. For example, height and weight which were closely correlated with BMI (weight [kg]/height [m]2) were excluded, whereas BMI, as an independent parameter, was included. Table 5 presents the results of multiple regression analysis. As is apparent, IOP was negatively correlated with refractive errors (b = 0.105, p < 0.0001), adjusting for age, sex and other possible confounding variables. It was positively correlated with BMI (b = 0.040, p < 0.0001), systolic blood pressure (b = 0.012, p = 0.001), fasting plasma glucose (b = 0.006, p < 0.0001) and total cholesterol (b = 0.003, p < 0.0001), adjusting for age, sex and other confounding variables. The male sex was positively correlated with IOP (b = 0.190, p = 0.032). In contrast to the univariate analysis findings, diastolic blood pressure, history of smoking, migraine or cold hands/feet were not significantly correlated with IOP (all p > 0.05). Age also was not significantly correlated with IOP (p = 0.298).
Discussion The present study represents the largest population-based survey (total participants: 13 431) in Korea for assessment of GAT-measured IOP distribution. The survey was conducted nationwide
Fig. 2. Overall distribution of intraocular pressure (IOP) among 13 428 subjects. (A) Distribution of IOP among total 13428 subjects. (B) Distribution of IOP among male subjects (n = 5799). (C) Distribution of IOP among female subjects (n = 7629). *All of the data are from the right eye.
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Table 2. Mean intraocular pressure (IOP) in the right eye as stratified by age and sex. Subgroup
Age (years)
Mean SD
Males (n = 5799)
≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70
13.94 14.29 14.44 14.36 13.94 13.72 13.58 13.80 13.83 13.90 14.01 13.69 13.77 14.05 14.14 14.13 13.97 13.70
Females (n = 7629)
Total (n = 13428)
2.70 2.70 2.80 2.89 2.79 2.79 2.71 2.68 2.67 2.68 2.62 2.84 2.71 2.70 2.75 2.80 2.70 2.82
95% Confidence interval
n
13.69–14.19 14.04–14.53 14.20–14.67 14.12–14.60 13.71–14.16 13.45–13.99 13.34–13.82 13.60–14.00 13.63–14.03 13.70–14.09 13.79–14.23 13.42–13.95 13.56–13.97 13.86–14.24 13.95–14.32 13.95–14.31 13.79–14.15 13.48–13.92
767 1085 1153 997 998 799 990 1509 1461 1380 1196 1093 1757 2594 2614 2377 2194 1892
Table 3. Prevalence of high intraocular pressure (IOP)† in right eye as stratified by age and sex. Subjects
Age (years)
IOP ≥ 22 mmHg n (%)
IOP ≥ 99.5th percentile n (%)
Males (n = 5799)
≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70 ≤29 30–39 40–49 50–59 60–69 ≥70
1 1 5 4 3 4 2 1 4 2 2 6 3 2 9 6 5 10
7 10 16 13 8 7 2 6 6 9 4 13 9 16 22 22 12 20
Females (n = 7629)
Total (n = 13428)
†
(0.10) (0.09) (0.56) (0.44) (0.26) (0.54) (0.21) (0.05) (0.36) (0.14) (0.12) (0.38) (0.16) (0.07) (0.46) (0.29) (0.19) (0.44)
(0.78) (1.06) (1.48) (1.51) (0.87) (1.42) (0.21) (0.43) (0.53) (0.65) (0.31) (1.30) (0.51) (0.75) (1.01) (1.08) (0.58) (1.34)
High IOP was defined as IOP ≥ 22 mmHg or IOP ≥ 99.5th percentile.
to minimize the effects of regional IOP variability which has been reported by (Suh & Kee 2012). The mean IOP was 13.99 2.75 mmHg, which was similar to that reported previously of a Korean population (14.10 2.74 mmHg) (Suh & Kee 2012). However, it is lower than those of previous reports from other Asian countries (14.6 2.7 mmHg in Tajimi study; 16.11 3.39 mmHg in Beijing eye study; 14.54 0.11 mmHg in Tanjong Pagar study (Iwase et al. 2004; Xu et al. 2005; Yip et al. 2007)). The explanation for our lower IOP is unclear, although just the specific population difference, or some other, unknown factor, might have been sufficient.
In the frequency distribution for IOP, even numbers generally were more common than odd (Fig. 2). We speculated that this reflected a kind of GAT-dial digit preference known as the ‘hedgehog effect’ (Buller et al. 2005) and that it might diminish the statistical power of the present study (Hessel 1986). We found a significantly higher mean mean IOP among males (14.19 2.78 mmHg) than females (13.79 2.70 mmHg). Moreover, adjusting for age and other confounding variables, the male sex showed a significantly positive correlation with IOP. On the contrary, another Korean study found no significant intergender difference in IOP (Suh &
Kee 2012). Certainly, IOP difference by sex remains controversial; possible cause of the divergent results to-date ismultiple confoundingfactors (e.g.geneticfactors, metabolic factors). With respect to age, our IOP readings were not significantly correlated with it, adjusting for potential confounding factors. This conflicted with previous East Asian studies conducted in Korea (Suh & Kee 2012) and Japan (Shiose 1984; Kawase et al. 2008; Tomoyose et al. 2010), which showed a negative correlation between IOP and age. Some reports have ascribed the discrepancies in age-related IOP changes to differences in age-related systemic changes including BMI and blood pressure (Shiose 1984; Shiose & Kawase 1986; Tomoyose et al. 2010). In this light, we supposed that IOP was not significantly correlated with age in our results, because precisely adjusting for various systemic factors including BMI and blood pressure was performed. Meanwhile, in the comparison of the mean IOP among the age groups subdivided by 10 years, the values increased between 19 and 49 years, peaking for the 40–49 years group. Then, the mean IOP decreased for the subjects above 50 years of age (Table 2). These findings carry significance in that there have been few reports on IOP among young adults ( 1/2 CT ref: Lens: Normal crystalline lens (n = 9141) Cataract (n = 3621) Pseudophakia (n = 662) Aphakia (n = 6)
Coefficient b 0.0004 0.396 0.007 0.020 0.010 0.076 0.016 0.027 0.241 0.010 0.038 0.487 0.005 0.007 0.005 0.002 0.329 0.242 0.109 0.381 0.268 0.085 1 0.280 0.067 1 0.029 0.089 0.828
Standard error
p-value
0.002 0.052 0.003 0.003 0.007 0.009 0.002 0.003 0.073 0.001 0.064 0.112 0.001 0.003 0.002 0.000 0.099 0.058 0.209 0.201 0.059 0.015
0.841†
